{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling categorical variables with KernelSHAP "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Note\n",
    "    \n",
    "To enable SHAP support, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[shap]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shap.summary_plot currently doesn't work with matplotlib>=3.6.0,\n",
    "# see bug report: https://github.com/slundberg/shap/issues/2687\n",
    "!pip install matplotlib==3.5.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we show how the KernelSHAP method can be used for tabular data, which contains both numerical (continuous) and categorical attributes. Using a logistic regression model fitted to the `Adult` dataset, we examine the performance of the KernelSHAP algorithm against the exact shap values. We investigate the effect of the background dataset size on the estimated shap values and present two ways of handling categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from alibi.explainers import KernelShap\n",
    "from alibi.datasets import fetch_adult\n",
    "from scipy.special import logit\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fetch_adult` function returns a `Bunch` object containing the features, the targets, the feature names and a mapping of categorical variables to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'target_names', 'category_map'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult = fetch_adult()\n",
    "adult.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = adult.data\n",
    "target = adult.target\n",
    "target_names = adult.target_names\n",
    "feature_names = adult.feature_names\n",
    "category_map = adult.category_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for your own datasets you can use our utility function `gen_category_map` to create the category map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alibi.utils import gen_category_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "data_perm = np.random.permutation(np.c_[data, target])\n",
    "data = data_perm[:,:-1]\n",
    "target = data_perm[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 30000\n",
    "X_train,y_train = data[:idx,:], target[:idx]\n",
    "X_test, y_test = data[idx+1:,:], target[idx+1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create feature transformation pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create feature transformation pipeline\n",
    "Create feature pre-processor. Needs to have 'fit' and 'transform' methods. Different types of pre-processing can be applied to all or part of the features. In the example below we will standardize ordinal features and apply one-hot-encoding to categorical features.\n",
    "\n",
    "Ordinal features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_features = [x for x in range(len(feature_names)) if x not in list(category_map.keys())]\n",
    "ordinal_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),\n",
    "                                      ('scaler', StandardScaler())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = list(category_map.keys())\n",
    "categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),\n",
    "                                          ('onehot', OneHotEncoder(drop='first', handle_unknown='error'))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in order to be able to interpret the coefficients corresponding to the categorical features, the option `drop='first'` has been passed to the `OneHotEncoder`. This means that for a categorical variable with `n` levels, the length of the code will be `n-1`. This is necessary in order to avoid introducing feature multicolinearity, which would skew the interpretation of the results. For more information about the issue about multicolinearity in the context of linear modelling see [[1]](#References).\n",
    "<a id='src_1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine and fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [0, 8, 9, 10]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [1, 2, 3, 4, 5, 6, 7, 11])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [0, 8, 9, 10]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;))]),\n",
       "                                 [1, 2, 3, 4, 5, 6, 7, 11])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[0, 8, 9, 10]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[1, 2, 3, 4, 5, 6, 7, 11]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 [0, 8, 9, 10]),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('onehot',\n",
       "                                                  OneHotEncoder(drop='first'))]),\n",
       "                                 [1, 2, 3, 4, 5, 6, 7, 11])])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor = ColumnTransformer(transformers=[('num', ordinal_transformer, ordinal_features),\n",
    "                                               ('cat', categorical_transformer, categorical_features)])\n",
    "preprocessor.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_proc = preprocessor.transform(X_train)\n",
    "X_test_proc = preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a binary logistic regression classifier to the Adult dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500, multi_class=&#x27;multinomial&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500, multi_class=&#x27;multinomial&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=500, multi_class='multinomial', random_state=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(multi_class='multinomial',\n",
    "                                random_state=0,\n",
    "                                max_iter=500,\n",
    "                                verbose=0,\n",
    "                               )\n",
    "classifier.fit(X_train_proc, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S/iNpl5jeCZietNyMmFZdelpewnXOFT0Byr5Koa2kCUnTw8xsWIZ1mgBtgF2BXYBHJW2ec0az2IlzzhU3xVd2Ksysd457mAE8GUfpfVvSj0BbYCawadJynWMaadKr5VUKzrkSIKTsXjX0FPBzgHhTrBlQAYwCjpDUXFI3oDvwNjAe6C6pm6RmhBtrozLtxEu4zrmSUItgmrqdEUA/QtXDDOBKYDgwPDYVWwEMjqXdjyQ9SrgZVgmcbmYr43bOAMYA5cBwM/so07494DrnSkJZzW+IrcHMjqxm1jHVLD8EGFJF+mhgdC779oDrnCt+udXhFi0PuM65oidqVT9bNDzgOudKggdc55wrEA+4zjlXIB5wnXOuEAQq84DrnHN1zm+aOedcAXnAdc65Qin9eOsB1zlXAuQlXOecKxgPuM45VwBCeetLoT55wHXOlYbSL+B6wHXOlQCvw3XOucLxgOuccwXiAdc55wrEH+11BXXrH47mgD17UrHwW3Y/4s8A9NyqEzddfAQtmjelsvJHLrjuESZ+/BWH9e/N2b/ZD0ksWbqc8699hA8nhzHu9tltW/5y/qGUl5Xx4NNv8rf7n6/Pw2o0zrj6Ica8/iFtW6/PW49cBsAfbv4nY177kKZNy+nWuS23X3EMrdZvyQ+VKznrT//g/U+ns3Llj/x6YB/OO/6Aej6C+lPL8cpStzUcOBCYa2Y9U+adD9wAtDOzCoWd3gwMBJYCx5nZxLjsYODyuOqfzOz+TPsu2nYWkvpJWizpvfi6Imlef0mfSZoi6eKk9Fck9Y7vu0maLKnBXKUjnhnHoWfdvkbaH888mOvvfpa9jr6Wv/zvM/zxrIMB+GrWfH5x6t/Y48g/89d7nmPopWFUkbIy8dcLD+ews+9g18P/xP/svzNbd9uk0IfSKB154K48fsvpa6T9vO82vDnyUt4YcSlbbLYxN903FoCnXpjI9ysqeXPkZbz84EXc9883mDZrfn1ku2jkcRDJ+4D+VWx/U2B/YFpS8gDCwJHdgVOAO+OybQhjofUF+gBXSmqdaccFDbiSmklaN4dVXjOzneLr6riNcuB2wonoARwpqUfKfjoDzwHnm9mYPGW/3r357ucs/GbpGmlmsP66LQDYYL11+HreYgDenjSVxd8uA2D8B1PpuPGGAOy8XVe+mF7BVzPn80PlSp58fiIDf7ZD4Q6iEduj15a03qDlGml777otTZqUA7BLz27MmrMICMFl6bIVVFauZPnyFTRrWr7qc26s8hVwzexVYEEVs4YCFwKWlDYIeMCCccCGkjoABwDPm9kCM1sIPE8VQTxVQaoUJG0LnAT8Kr7ercXm+gBTzOyLuO2RhJPycZzfAXgAuMzMMg5bXOouvelxnrj1dK45+xAk0f/EG9da5thBu/PCm+H0dGjXiplzFq6aN2vOQnbu2bVQ2XVpPDTqLQ7ZrxcAg/b5CaP/M4ltBlzGsuUrGHLur2jdKpeySgOUfY1CW0kTkqaHmdmwtJuWBgEzzez9lKDdCZieND0jplWXnladBdxYkj0cODEm3QtcZWbfxvlDiePApxhpZtfG97tJeh+YBVwQhyGu6kD7Jk3fD1xuZo+nydsphD8PoOl6OR5ZcTnhf37KpTc9yb9efo+D9/0Jt/zhaA45/bZV8/fcuTvHHLQbA04eWo+5dJncMPw5mjQp4/ABuwDwzkdfUl5WxifPDmHRN0sZePJQ+vXZhq6d29ZzTutPDnW4FWbWO4fttgQuJVQn1Km6LOHOBiYBJ5nZp6kzzezcDOtPBLqY2RJJA4GnCPUombwAHCPpPjNbWtUC8dduGEBZy42tqmVKxZEH9uXiG8Nvy1MvvMvNlx21at52W3bklsuP4rCz72Th4u8AmD1vMZ3ar65q6ti+NbNjNYSrHw//axxjX/+Qp+44a1VQefy5Ceyzew+aNimnXZv16bvj5rz7ybRGG3ClcP+hjmwBdAMSpdvOwERJfYCZwKZJy3aOaTOBfinpr2TaUV3W4R5KyNSTkq6Q1CV5pqShSTfEkl8XA5jZN2a2JL4fDTSV1JbqT0DC9cB44DFJDb4Vxux5i9mjV/gd2muXrfhi+jwAOrdvzQPXn8xpVz7A59Pmrlp+4sdfscVm7dis40Y0bVLOr/brxbOvTqqXvDt44c2PueXBF3j4xlNp2aLZqvTOm7ThtfGfAfDdsu+Z8OGXdO/avr6yWQSyq7+tSUsGM/vAzDY2s65m1pXwV3MvM/saGAX8RsGuwGIzmw2MAfaX1DreLNs/pqVVZwHJzMYCYyVtBBwDPC2pglDi/TJTCVfSJsAcM7P4S1MGzAcWAd0ldSME2iOAo1JWPwd4GLhH0nFmVtKl2IS7/3Qce+zcnY02XI8Pn7mGa4eN5pwhD/OX8w+lSXkZy1dUcs6fRwDw+5MG0KbVutxw0a8BqKz8kb0HX8/KlT9y4fWP8sQtp1NeLv4xahyffvF1fR5Wo3HiZffyxjuTmb9oCdv94nIuPmUgQ+8by/crKldVA/XevitDLzmSkw7bizOufojdDv8TBhz1y13p2T1jFWGDlq/nHiSNIJRO20qaAVxpZvdUs/hoQpOwKYRmYccDmNkCSdcQCncAV5tZVTfi1tx3IWNRDJyzzWx6FsueAfwWqASWAeeZ2Ztx3kDgb0A5MNzMhsT0Vwh1vRMkNQOeAd43s99Xt5+ylhtb860Pr9VxucJaOP62zAu5orFH3968886EWoXLFptsZV0G35rVsv93ff93cqnDLaSC/sltZm/nsOxtQJXfrFjFMLqK9H5J71dQgEpw51wBKH8l3PrU4Os4nXOlT9TpTbOC8YDrnCsJHnCdc64QvErBOecKQ3j3jM45VyD56y2sPnnAdc6VhAYQbz3gOudKQN0+2lswHnCdc0XP63Cdc66AGkC89YDrnCsNXsJ1zrkCaQDx1gOuc64EyEu4zjlXEELeSsE55wqlARRwPeA650qDVyk451whNJDOa+pyTDPnnMuLxIMP+RjTTNJwSXMlfZiU9ldJn0qaJOmfkjZMmneJpCmSPpN0QFJ6/5g2JTEWYyYecJ1zJSGPg0jeB/RPSXse6GlmOwD/B1wS99mDMG7idnGdOySVSyoHbgcGAD2AI+OyaXnAdc6VhLIyZfXKxMxeBRakpI01s8o4OY4wGjjAIGCkmX1vZlMJg0n2ia8pZvZFHM5rZFw2/TFke7DOOVdvYh1uNi/CaLwTkl6n5Li3E4Bn4/tOQPKgtzNiWnXpaflNM+dc0VNu/eFW1HTUXkmXEUYK/0dN1s/EA65zriTUdSsFSccBBwL7mJnF5JnApkmLdY5ppEmvllcpOOdKQpmU1asmJPUHLgQOMrOlSbNGAUdIai6pG9AdeBsYD3SX1E1SM8KNtVGZ9uMlXOdc0VMeOyCXNALoR6jrnQFcSWiV0Bx4PlZdjDOz08zsI0mPAh8TqhpON7OVcTtnAGOAcmC4mX2Uad8ecJ1zJSFfXSmY2ZFVJN+TZvkhwJAq0kcDo3PZtwdc51xJaNCP9kq6FbDq5pvZWXWSI+ecq0IDiLdpS7gTCpYL55xLQ4SmYaWu2oBrZvcnT0tqmXL3zjnnCqYBdIebuVmYpN0kfQx8Gqd3lHRHnefMOecSlN1jvcXeSXk27XD/BhwAzAcws/eBveowT845twZRt+1wCyWrVgpmNj3lDuHKusmOc85VrchjaVayCbjTJe0OmKSmwNnAJ3WbLeecW1NDaBaWTZXCacDphJ5wZgE7xWnnnCuIbHsKK/aYnLGEa2YVwNEFyItzzlWrvNijaRayaaWwuaR/SZoXh6V4WtLmhcicc84l5HHEh3qTTZXCw8CjQAegI/AYMKIuM+Wcc8lCK4XsXsUsm4Db0sweNLPK+HoIaFHXGXPOuVWyLN0Wewk3XV8KbeLbZ+OIlCMJfSv8mhx7yHHOudoq8lialXQ3zd4hBNjEYZ6aNM+Io1o651whFHvpNRvp+lLoVsiMOOdcdQSUF3sFbRayetJMUk/C2Our6m7N7IG6ypRzzqUq/XCbXbOwK4Fb4+vnwPXAQXWcL+ecW0XKX18KkobHJq4fJqW1kfS8pMnx/9YxXZJukTRF0iRJvZLWGRyXnyxpcDbHkU0rhUOBfYCvzex4YEegVTYbd865fMnjk2b3Af1T0i4GXjSz7sCLcRpgAGHgyO7AKcCdIS9qQxgLrS/QB7gyEaTTySbgLjOzH4FKSRsAc1lzeGDnnKtz+WoWZmavAgtSkgcBiT7A7wcOTkp/wIJxwIaSOhB6UHzezBaY2ULgedYO4mvJpg53gqQNgbsILReWAG9lsZ5zzuVNDo0U2kpKHrFmmJkNy7BOezObHd9/DbSP7zsB05OWmxHTqktPK5u+FH4X3/5d0nPABmY2KdN6zjmXL5JyaaVQYWa9a7ovMzNJ1Y7nWBvpHnzolW6emU2siww551xV6rgd7hxJHcxsdqwymBvTZ7JmFWrnmDYT6JeS/kqmnaQr4d6YZp4Be2faeCnYYZtNGfufofWdDZeD6fN9aL1SsqLyx7xsJ5sbTrUwChgMXBv/fzop/QxJIwk3yBbHoDwG+HPSjbL9yeJhsHQPPvy8Fpl3zrm8Efkr4UoaQSidtpU0g9Da4FrgUUknAl8Bh8fFRwMDgSnAUuB4ADNbIOkaYHxc7mozS70Rt5asHnxwzrn6lq8HzczsyGpm7VPFskY1Ay6Y2XBgeC779oDrnCt6UiN6tNc55+pbA4i3WT3aK0nHSLoiTm8mqU/dZ80551ZrCGOaZXPj7w5gNyBR7/EtcHud5cg551KEER/y05dCfcqmSqGvmfWS9C6AmS2U1KyO8+Wcc2uo42ZhBZFNwP1BUjmh7S2S2gH5aVjnnHNZKvLCa1ayCbi3AP8ENpY0hNB72OV1mivnnEuS46O9RSubvhT+IekdQhs1AQeb2Sd1njPnnEvSAOJt5oAraTPCExb/Sk4zs2l1mTHnnEtI3DQrddlUKfyb1YNJtgC6AZ8B29Vhvpxzbg0NIN5mVaWwffJ07EXsd9Us7pxz+adGUqWQyswmSupbF5lxzrnqqAEMI5lNHe55SZNlQC9gVp3lyDnnUgho0gAa4mZTwl0/6X0loU73ibrJjnPOVa2OOyAviLQBNz7wsL6ZXVCg/Djn3FpCK4X6zkXtpRtip4mZVUrao5AZcs65tZRAxzTZSFcr8nb8/z1JoyQdK+lXiVchMueccwn57LxG0rmSPpL0oaQRklpI6ibpv5KmSHok0WeMpOZxekqc37XGx5DFMi2A+YQxzA4Efhn/d865ghBQXpbdK+O2pE7AWUBvM+sJlANHANcBQ81sS2AhcGJc5URgYUwfGperkXR1uBvHFgofsvrBh4Q6GULYOeeqJsry2yysCbCOpB+AlsBsQqHyqDj/fuAq4E5gUHwP8DhwmyTF4Xdy3ml1yoH1oMqj9IDrnCuYMIhk1ou3lTQhaXqYmQ1LTJjZTEk3ANOAZcBY4B1gkZlVxsVmAJ3i+07A9LhupaTFwEZARa7HkS7gzjazq3PdoHPO5V1uT5pVmFnvajcVhjYfROimYBHwGNC/ljnMSrqA2wDuCTrnGoo8dl6zLzDVzOYBSHoS2APYMNE6C+gMzIzLzwQ2BWZIagK0ItzXylm6Kua1hgx2zrn6kKhSyNOYZtOAXSW1VHiaYh/gY+BlQn/fAIOBp+P7UXGaOP+lmtTfQpoSrpktqMkGnXOuLuSrA3Iz+6+kx4GJhKdn3wWGEZ6iHSnpTzHtnrjKPcCDkqYACwgtGmrEh0l3zhU9kd8xzczsSuDKlOQvgLVGJDez5cBh+divB1znXPFTI+hLwTnnikXph1sPuM65EtCYhthxzrl6V/rh1gOuc64kiLIG0D+jB1znXNHLdyuF+uIB1zlXEryVgnPOFUjph1sPuM65UuDtcJ1zrjAElHvAdc65wij9cOsB1zlXIhpAAdcDrnOu+IVmYaUfcT3gOudKgpdwnXOuIIS8hOucc3XPWyk451yhZD98TlFrCI8nO+cagTyOaYakDSU9LulTSZ9I2k1SG0nPS5oc/28dl5WkWyRNkTRJUq+aHoMHXOdcSVCW/7J0M/CcmW0D7Ah8AlwMvGhm3YEX4zTAAKB7fJ0C3FnTY/CA65wreqED8uxeGbcltQL2Ig4SaWYrzGwRMAi4Py52P3BwfD8IeMCCcYTh1DvU5Dg84DrnSkKZlNULaCtpQtLrlJRNdQPmAfdKelfS3ZLWBdqb2ey4zNdA+/i+EzA9af0ZMS1nftPMOVcScqguqDCz3mnmNwF6AWfGIdNvZnX1AQBmZpKsZjlNv2NXgmbNWci5f36YigXfIsFRv9yNEw772ar5w0a+zJA7RvHuqGtos+F6jH3tA26851nKykR5eRlXnnkIu+yweT0eQePy/YofOP6Cv7Pih0pWrvyRfX+6Pacfuz9mxq33j+H51yZRVlbG4b/YlaMP3pNvvl3KFUMfY/qs+TRv1pQ/nncY3btuUt+HUW8SVQp5MgOYYWb/jdOPEwLuHEkdzGx2rDKYG+fPBDZNWr9zTMtZUQdcSfcBPwMWx6TjzOw9hX7abgYGAktj+kRJXYFnzKxnXP9k4DRgXzNbWOj816Xy8jIu/91BbL/1pixZupwDT7qJPXfZmq26bsKsOQt5bfxndGrfetXye+y8Ffvt2RNJfPL5LE6/8n5eeuiSejyCxqVZ0ybcfd0ptFynOT9UrmTw+XewZ++tmTp9Ll/PW8TTd11AWVkZ8xctAeCukS+x9eYd+dsVg5k6fS5Dbn+Ku69N/cu4Mcnfgw9m9rWk6ZK2NrPPgH2Aj+NrMHBt/P/puMoo4AxJI4G+wOKkqoec1GsdbqLZRQa/N7Od4uu9mJbxrqGkY4EzgQMaWrAFaN+2FdtvHX5012vZgi27tGfOvPC7dPVtT3HJb3+5RhOZdVs2X9Wf6NJlKwqe38ZOEi3XaQ5AZeVKKitXIolHnxnHaUfvS1lZ+CputOF6AHwxbS59dtoSgG6bbsysOQuYv/Db+sl8MciySVgObXXPBP4haRKwE/BnQqDdT9JkYN84DTAa+AKYAtwF/K6mh1HfJdwJksYR7ha+bGbZ1pmsumsIjItt6lbdNZR0OOFPhH3MrCLvuS4y02cv4KPJM9ipRxfGvvYBm7RtRY8t167Tf+7VSVw/7N9ULFzCvdedXA85bdxWrvyRI868mWmz5nPEL3dnh202Y/rs+Tz3n/d56c0Pad1qPS7+7UF06dSOrTbvwItvfMDOPbvxwWfTmD1nEXMqFrNR6/Xr+zDqTT6fe4iFt6rqefepYlkDTs/Hfuu7lcJWwAjgDOBjSZdK6piyzJDY2HiopOYxLd1dwy7AbcD+ZvZ1VTuVdEriDub8itKOx98t/Z7T/nAvV5x5CE3Ky7j9oRc478QBVS7bf68deOmhS7hryAnceM/oAufUlZeX8dgd5/L8Q5fx4WfTmPzl16z4oZLmzZow8taz+Z/+fbjipscAOPHwn/PtkuUc9ruhjHj6TbbZouOqUnBjlHi0N5tXMavXT9DMVprZM2b2K0K7uM2BaZL6xEUuAbYBdgHaABdlsdl5wDTg8DT7HWZmvc2s90Zt29bqGOrTD5UrOe0P93Lwfjsz4Gc78NXMCqbPXsCAE/7KHodfzex5i/nFSTcyd/43a6zXd6ctmDZrPgtifaErrA3WW4dddtyCNyZ8Rvu2rdhnj+0B2GePnkyeGsoI663bgmvOP5zH7jiXIb//NQsXf0fnTdrUZ7brn7J8FbF6/8mU1ErSqYSK6e7ACcAkADObHRsbfw/cCyQCcbq7hksJN9NOk3R0AQ6hXpgZF143ki27tOfkX/cDYJstOjJx1DW88egVvPHoFXRo14p/330+G2+0AV/OmEeixuaDz6az4oeVtG61bj0eQeOyYNESvlmyDIDl3//AWxMn023Tduy9+3aMf/9zACZM+oIunUIB4Jsly/jhh0oAnnjubXpt34311m1RP5kvEnl+0qxe1GsdrqSHgN2Ax4DfmNnklPmJJhoiPPXxYZxV5V3D2EoBM5srqT/wiqQKMxtTmCMqnAkfTOXJMRPYZvMODDjhrwD8/uRfsPduPapc/tn/TOKJMeNp2qSc5s2bcvtVv2kQg/KViooF33L5jY+wcuWP/GjGAXvtwM/69uAn23XjkutG8OA/X6Nli2Zcde6hAEydNpfLb3wEgC27bMIfY3pj1hAuV2V/n6oOdi4dBIw2s8pq5r8EtCP8ofAecJqZLYkB+DagP6FEe7yZTaiiWdiOhDuMh5jZ21XtY6deO9vY/4zL74G5OrV46Q/1nQWXg1/tvycfvD+xVuFy2+1/Yg88/UpWy/bZYsN3Mjz4UG/qtYRrZqMyzN+7mvQq7xqa2ZdAz6Tp96nhI3jOuSLTAEq49d0szDnnMpJI9JNQ0jzgOudKQumHWw+4zrlS0QAirgdc51wJKP4mX9nwgOucKwkNoArXA65zrvgJD7jOOVcwXqXgnHMF4iVc55wrkAYQbz3gOudKQAn0BJYND7jOuZLQEOpw6717RuecyyQxiGQ2r6y2J5XHIdKfidPdJP1X0hRJj0hqFtObx+kpcX7X2hyHB1znXGnIbwfkZwOfJE1fBww1sy2BhcCJMf1EYGFMHxqXqzEPuM65kpCvDsgldQZ+AdwdpwXsTRguHeB+Qv/bEMZPvD++fxzYR7XoSNoDrnOuJOQwam/bxJiF8ZU6vvzfgAuBH+P0RsCipH65k8dIXDV+Ypy/OC5fI37TzDlXEnIoVlZU1wG5pAOBuWb2jqR+eclYDjzgOudKQ34aKewBHCRpINAC2AC4GdhQUpNYik0eIzExfuIMSU2AVsD8mu7cqxScc0Uv0QF5Nq90zOwSM+tsZl2BI4CXzOxo4GUgMXDcYODp+H5UnCbOf8lqMS6ZB1znXEmo41HSLwLOkzSFUEd7T0y/B9gopp8HXFzzXXiVgnOuVOT5uQczewV4Jb7/AuhTxTLLgcPytU8PuM65EuAdkDvnXMF4b2HOOVcA3gG5c84VkFcpOOdcgXgJ1znnCqQBxFsPuM65EiAv4TrnXAGVfsT1gOucK3qJDshLnQdc51xJ8CoF55wrEG8W5pxzhVL68dYDrnOuNDSAeOsB1zlX/OTNwpxzrnBqMXZj0fCA65wrCaUfbn3EB+dcichh1N4M29Gmkl6W9LGkjySdHdPbSHpe0uT4f+uYLkm3SJoiaZKkXjU9Bg+4zrkSoKz/ZaESON/MegC7AqdL6kEYPudFM+sOvMjq4XQGAN3j6xTgzpoehQdc51zRS/SHm48SrpnNNrOJ8f23wCdAJ2AQcH9c7H7g4Ph+EPCABeMII/x2qMlxeMB1zpWEfAXcNbeprsBPgP8C7c1sdpz1NdA+vu8ETE9abUZMy5nfNHPOlYQcnjRrK2lC0vQwMxu21vak9YAngHPM7JvkVhBmZpJqPBx6dTzgOueKX26l1woz6512c1JTQrD9h5k9GZPnSOpgZrNjlcHcmD4T2DRp9c4xLWdepeCcK3rK4ZVxW6Eoew/wiZndlDRrFDA4vh8MPJ2U/pvYWmFXYHFS1UNOvITrnCsN+WuIuwdwLPCBpPdi2qXAtcCjkk4EvgIOj/NGAwOBKcBS4Pia7tgDrnOuJOSrtzAze53qw/c+VSxvwOn52LcHXOdcSfAOyJ1zrlA84DrnXGF4B+TOOVcAiSfNSp1CfXDjJWke4Y5kQ9MWqKjvTLicNNTPrIuZtavNBiQ9Rzg/2agws/612V9dafQBt6GSNCFT429XXPwza/j8wQfnnCsQD7jOOVcgHnAbrrU663BFzz+zBs7rcJ1zrkC8hOuccwXiAdc55wrEA24DJKmfpMWS3ouvK5Lm9Zf0WRwQ7+Kk9Fck9Y7vu8WB9A6oj/w3BpLukzQ16TPaKaZXOWChpK6SPkxa/2RJ7yQGOnSlwZ80KxGSmgFNzey7LFd5zcwOTNlGOXA7sB9hmJDxkkaZ2cdJy3QGniMMsjcmP7lvfCS1NrOFGRb7vZk9npKWPGBhX8KAhX1Ttn0scCawdxb7cEXES7hFTtK2km4EPgO2quXm+gBTzOwLM1sBjCQMkJfQARgLXGZmo2q5r8ZugqR/SNpbyumh1LQDFko6nDCa7P5m1hCfSmvQPOAWIUnrSjpe0uvAXcDHwA5m9m6cPzTpT9Hk18VJm9lN0vuSnpW0XUzLNBje/cBtVZS6XO62AkYAZwAfS7pUUseUZYbEaoOhkprHtHSfURfgNkKw/boO8+7qiFcpFKfZwCTgJDP7NHWmmZ2bYf2JhOfXl0gaCDxF+BM1kxeAYyTdZ2ZLc8yzS2JmK4FngGcktQP+AkyTtLuZvQ1cQhgZthmh/e1FwNUZNjsPWEAYiWBoXeXd1R0v4RanQwmD1D0p6QpJXZJnZirhmtk3ZrYkvh8NNJXUlsyD4V0PjAcek+Q/xrUkqZWkUwljYnUHTiD8kGJms2O1wffAvYTqHkj/GS0lDPVymqSjC3AILs/8S1WEzGwsMFbSRsAxwNOSKggl3i8zlXAlbQLMiUM99yH8sM4HFgHdJXUjfImPAI5KWf0c4GHgHknHmT8ZUyOSHgJ2Ax4DfmNmk1PmJ0aHFXAwkGiBMAo4Q9JIws2yxXG5rgBmNldSf+AVSRV+Y7O0eMAtYmY2H7gZuDkGzpVZrnoo8FtJlcAy4IgYOCslnQGMAcqB4Wb2Uco+TdJgwp/D1wO/z8/RNDqPAseZWWU18/8RqxoEvAecFtMzDlhoZlMlHQSMlnRIrKJwJcAf7XXOuQLxOlznnCsQD7jOOVcgHnCdc65APOA651yBeMB1zrkC8YDr0pK0Mj5U8aGkxyS1rMW27pN0aHx/t6QeaZbtJ2n3Guzjy/iQR1bpKcssyXFfV0m6INc8usbLA67LZJmZ7WRmPYEVrG4vCkBNn0gzs5OSeymrQj8g54DrXDHzgOty8RqwZSx9viZpFKFjlnJJf5U0PnbGciqs6tv1NoX+d18ANk5sSGv2v9tf0sTY2c6L8amq04BzY+n6p5LaSXoi7mO8pD3iuhtJGivpI0l3Ex4kSEvSUwp9yX4k6ZSUeUNj+ovxwQQkbSHpubjOa5K2ycvZdI2OP2nmshJLsgMIfeUC9AJ6xqeeTiE8grpL7PXqDUljgZ8AWwM9gPaEXs+Gp2y3HaFHtL3ittqY2QJJfweWmNkNcbmHgaFm9rqkzQhPy20LXAm8bmZXS/oFcGIWh3NC3Mc6hD6Bn4hP9a0LTDCzcxU6bb+S0NvXMOA0M5ssqS9wB7B3DU6ja+Q84LpM1pH0Xnz/GnAP4U/9t81sakzfH9ghUT8LtCJ01rIXMCL2nDVL0ktVbH9X4NXEtsxsQTX52BfoodVdy24gab24j1/Fdf8tKZsOuc+SdEh8v2nM63zgR+CRmP4QofOg9eLxPpa07+Y4VwMecF0my8xsp+SEGHiSR54QcGZqRyoKXUPmSxmwq5ktryIvWZPUjxC8dzOzpZJeAVpUs7jF/S5KPQfO1YTX4bp8GEPoLKcpgKStJK0LvAr8OtbxdgB+XsW644C9Yg9mSGoT078F1k9abixhWBnicjvFt68SezyTNADINMZXK2BhDLbbEErYCWWEjn+I23zdzL4Bpko6LO5DknbMsA/nquQB1+XD3YT62YkKAx3+L+Gvp38Ck+O8B4C3Ulc0s3nAKYQ/399n9Z/0/wIOSdw0A84Cesebch+zurXEHwkB+yNC1cK0DHl9Dmgi6RPgWkLAT/gO6BOPYW9Wdwh+NHBizN9HrDkskXNZ897CnHOuQLyE65xzBeIB1znnCsQDrnPOFYgHXOecKxAPuM45VyAecJ1zrkA84DrnXIH8Px+v2hDVxUkTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Confusion matrix for the logistic regression classifier'\n",
    "disp = ConfusionMatrixDisplay.from_estimator(classifier, \n",
    "                                             X_test_proc, \n",
    "                                             y_test,\n",
    "                                             display_labels=target_names,\n",
    "                                             cmap=plt.cm.Blues,\n",
    "                                             normalize=None,\n",
    "                                            )\n",
    "disp.ax_.set_title(title);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.855078125\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy: ', accuracy_score(y_test, classifier.predict(X_test_proc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intepreting the logistic regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to interpret the logistic regression model, we need to first recover the encoded feature names. The feature effect of a categorical variable is computed by summing the coefficients of the encoded variables. Hence, we first understand how the `preprocessing` transformation acts on the data and then obtain the overall effects from the model coefficients.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we are concerned with understanding the dimensionality of a preprocessed record and what it is comprised of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimensionality of a preprocessed record is (1, 49).\n",
      "Then number of continuos features in the original data is 4.\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "print(f\"The dimensionality of a preprocessed record is {X_train_proc[idx:idx+1, :].shape}.\")\n",
    "print(f\"Then number of continuos features in the original data is {len(ordinal_features)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, of 49, 45 of the dimensions of the original data are encoded categorical features. We obtain `feat_enc_dim`, an array with the lengths of the encoded dimensions for each categorical variable that will be use for processing the results later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    feature_names  encoded_dim\n",
      "0       Workclass            8\n",
      "1       Education            6\n",
      "2  Marital Status            3\n",
      "3      Occupation            8\n",
      "4    Relationship            5\n",
      "5            Race            4\n",
      "6             Sex            1\n",
      "7         Country           10\n",
      "The dimensionality of the encoded categorical features is 45.\n"
     ]
    }
   ],
   "source": [
    "fts = [feature_names[x] for x in categorical_features]\n",
    "# get feature names for the encoded categorical features\n",
    "ohe = preprocessor.transformers_[1][1].named_steps['onehot']\n",
    "cat_enc_feat_names = ohe.get_feature_names_out(fts)\n",
    " # compute encoded dimension; -1 as ohe is setup with drop='first'\n",
    "feat_enc_dim = [len(cat_enc) - 1 for cat_enc in ohe.categories_]\n",
    "d = {'feature_names': fts , 'encoded_dim': feat_enc_dim}\n",
    "df = pd.DataFrame(data=d)\n",
    "print(df)\n",
    "total_dim = df['encoded_dim'].sum() \n",
    "print(f\"The dimensionality of the encoded categorical features is {total_dim}.\")\n",
    "assert total_dim == len(cat_enc_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By analysing an encoded record, we can recover the mapping of column indices to the features they represent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t-0.8464456331823879\n",
      "  (0, 1)\t-0.14513571926899238\n",
      "  (0, 2)\t-0.21784551572515998\n",
      "  (0, 3)\t0.28898151525672766\n",
      "  (0, 7)\t1.0\n",
      "  (0, 15)\t1.0\n",
      "  (0, 19)\t1.0\n",
      "  (0, 21)\t1.0\n",
      "  (0, 32)\t1.0\n",
      "  (0, 37)\t1.0\n",
      "  (0, 47)\t1.0\n"
     ]
    }
   ],
   "source": [
    "print(X_train_proc[0, :])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.84644563 -0.14513572 -0.21784552  0.28898152]\n",
      "['Age', 'Capital Gain', 'Capital Loss', 'Hours per week']\n"
     ]
    }
   ],
   "source": [
    "numerical_feats_idx  = preprocessor.transformers_[0][2]\n",
    "categorical_feats_idx  = preprocessor.transformers_[1][2]\n",
    "scaler = preprocessor.transformers_[0][1].named_steps['scaler']\n",
    "print((X_train[idx, numerical_feats_idx] - scaler.mean_)/scaler.scale_)\n",
    "num_feats_names = [feature_names[i] for i in numerical_feats_idx]\n",
    "cat_feats_names = [feature_names[i] for i in categorical_feats_idx]\n",
    "print(num_feats_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, the first four columns of the encoded data represent the `Age`, `Capital Gain` `Capital Loss` and `Hours per week` features. Notice these features have a different index in the dataset prior to processing `X_train`. \n",
    "\n",
    "The remainder of the columns encode the encoded categorical features, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Workclass_1.0' 'Workclass_2.0' 'Workclass_3.0' 'Workclass_4.0'\n",
      " 'Workclass_5.0' 'Workclass_6.0' 'Workclass_7.0' 'Workclass_8.0'\n",
      " 'Education_1.0' 'Education_2.0' 'Education_3.0' 'Education_4.0'\n",
      " 'Education_5.0' 'Education_6.0' 'Marital Status_1.0' 'Marital Status_2.0'\n",
      " 'Marital Status_3.0' 'Occupation_1.0' 'Occupation_2.0' 'Occupation_3.0'\n",
      " 'Occupation_4.0' 'Occupation_5.0' 'Occupation_6.0' 'Occupation_7.0'\n",
      " 'Occupation_8.0' 'Relationship_1.0' 'Relationship_2.0' 'Relationship_3.0'\n",
      " 'Relationship_4.0' 'Relationship_5.0' 'Race_1.0' 'Race_2.0' 'Race_3.0'\n",
      " 'Race_4.0' 'Sex_1.0' 'Country_1.0' 'Country_2.0' 'Country_3.0'\n",
      " 'Country_4.0' 'Country_5.0' 'Country_6.0' 'Country_7.0' 'Country_8.0'\n",
      " 'Country_9.0' 'Country_10.0']\n"
     ]
    }
   ],
   "source": [
    "print(cat_enc_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain a single coefficient for each categorical variable, we pass a list with the indices where each encoded categorical variable starts and the encodings dimensions to the `sum_categories` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alibi.explainers.shap_wrappers import sum_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the start index of each categorical variable knowing that the categorical variables are adjacent and follow the continuous features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(ordinal_features)\n",
    "cat_feat_start = [start]\n",
    "for dim in feat_enc_dim[:-1]:\n",
    "    cat_feat_start.append(dim + cat_feat_start[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = classifier.coef_\n",
    "beta = np.concatenate((-beta, beta), axis=0)\n",
    "intercepts = classifier.intercept_\n",
    "intercepts =  np.concatenate((-intercepts, intercepts), axis=0)\n",
    "all_coef = sum_categories(beta, cat_feat_start, feat_enc_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract and plot feature importances. Please see [this](kernel_shap_wine_lr.ipynb) example for background on interpreting logistic regression coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_importance(class_idx, beta, feature_names, intercepts=None):\n",
    "    \"\"\"\n",
    "    Retrive and sort abs magnitude of coefficients from model.\n",
    "    \"\"\"\n",
    "    \n",
    "    # sort the absolute value of model coef from largest to smallest\n",
    "    srt_beta_k = np.argsort(np.abs(beta[class_idx, :]))[::-1]\n",
    "    feat_names = [feature_names[idx] for idx in srt_beta_k]\n",
    "    feat_imp = beta[class_idx, srt_beta_k]\n",
    "    # include bias among feat importances \n",
    "    if intercepts is not None: \n",
    "        intercept = intercepts[class_idx]\n",
    "        bias_idx = len(feat_imp) - np.searchsorted(np.abs(feat_imp)[::-1], np.abs(intercept) )\n",
    "        feat_imp = np.insert(feat_imp, bias_idx, intercept.item(), )\n",
    "        intercept_idx = np.where(feat_imp == intercept)[0][0]\n",
    "        feat_names.insert(intercept_idx, 'bias')\n",
    "\n",
    "    return feat_imp, feat_names\n",
    "\n",
    "def plot_importance(feat_imp, feat_names, class_idx, **kwargs):\n",
    "    \"\"\"\n",
    "    Create a horizontal barchart of feature effects, sorted by their magnitude.\n",
    "    \"\"\"\n",
    "    \n",
    "    left_x, right_x = kwargs.get(\"left_x\"), kwargs.get(\"right_x\")\n",
    "    eps_factor = kwargs.get(\"eps_factor\", 4.5)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    y_pos = np.arange(len(feat_imp))\n",
    "    ax.barh(y_pos, feat_imp)\n",
    "    ax.set_yticks(y_pos)\n",
    "    ax.set_yticklabels(feat_names, fontsize=15)\n",
    "    ax.invert_yaxis()                  # labels read top-to-bottom\n",
    "    ax.set_xlabel(f'Feature effects for class {class_idx}', fontsize=15)\n",
    "    ax.set_xlim(left=left_x, right=right_x)\n",
    "    \n",
    "    for i, v in enumerate(feat_imp):\n",
    "        eps = 0.03\n",
    "        if v < 0:\n",
    "            eps = -eps_factor*eps\n",
    "        ax.text(v + eps, i + .25, str(round(v, 3)))\n",
    "    \n",
    "    return ax, fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_idx = 0\n",
    "perm_feat_names = num_feats_names + cat_feats_names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Capital Gain',\n",
       " 'Capital Loss',\n",
       " 'Hours per week',\n",
       " 'Workclass',\n",
       " 'Education',\n",
       " 'Marital Status',\n",
       " 'Occupation',\n",
       " 'Relationship',\n",
       " 'Race',\n",
       " 'Sex',\n",
       " 'Country']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_feat_names # feats are reordered by preprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_imp, srt_feat_names = get_importance(class_idx, \n",
    "                                          all_coef, \n",
    "                                          perm_feat_names,\n",
    "                                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Marital Status',\n",
       " 'Education',\n",
       " 'Capital Gain',\n",
       " 'Occupation',\n",
       " 'Workclass',\n",
       " 'Race',\n",
       " 'Country',\n",
       " 'Sex',\n",
       " 'Relationship',\n",
       " 'Hours per week',\n",
       " 'Age',\n",
       " 'Capital Loss']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srt_feat_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, class_0_fig = plot_importance(feat_imp, \n",
    "                                 srt_feat_names, \n",
    "                                 class_idx,\n",
    "                                 left_x=-2.5,\n",
    "                                 right_x=3.7,\n",
    "                                 eps_factor=12  # controls text distance from end of bar\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in the above, the feature effects are with respect to the model bias, which has a value of $1.31$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.15990831 -1.17397349 -0.13215877 -0.17288254]\n",
      "3.2134915799542094\n"
     ]
    }
   ],
   "source": [
    "# Sanity check to ensure graph is correct.\n",
    "print(beta[class_idx, 0:4]) # Age, Capital Gains, Capital Loss, Hours per week \n",
    "print(np.sum(beta[class_idx, 18:21])) # Marital status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply KernelSHAP to explain the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='src_1'></a>\n",
    "\n",
    "Note that the *local accuracy* property of SHAP (eq. (5) in [[1]](#References))  requires\n",
    "\\begin{equation*}\n",
    "f(x) = g(x') = \\phi_0 + \\sum_{i=1}^M \\phi_i x_i'.\n",
    "\\label{eq:local_acc} \\tag{1}\n",
    "\\end{equation*}\n",
    "Hence, sum of the feature importances should be equal to the model output, $f(x)$. By passing `link='logit'` to the explainer, we ensure that $\\phi_0$, the *base value* (see _**Local explanation**_ section [here](kernel_shap_wine_intro.ipynb)) will be calculated in the margin space (i.e., a logit transformation is applied to the probabilities) where the logistic regression model is additive.\n",
    "\n",
    "Further considerations when applying the KernelSHAP method to this dataset are:\n",
    "\n",
    "- ***the background dataset size***: by setting a larger value for the `stop_example_idx` in the set below, you can observe how the runtime of the algorithm increases. At the same time, it is important to have a diverse but sufficiently large set of samples as background so that the missing feature values are correctly integrated. A way to reduce the number of samples is to pass the `summarise_background=True` flag to the explainer `fit` option along with the desired number of samples (`n_background_samples`). If there are no categorical variables in the data and there is no data  grouping, then a k-means clustering algorithm is used to summarise the data. Otherwise, the data is sampled uniformly at random. Below, we used the `train_test_split` function of `sklearn` instead so that the label proportions are approximately the same as in the original split. \n",
    "- ***the number of instances to be explained***: the test set contains a number of `2560` records, which are $49$-dimensional after pre-processing, as opposed to $13$-dimensional as in the Wine dataset example. For this reason, only a fraction of `fraction_explained` (default $5\\%$) are explained by way of getting a more general view of the model behaviour compared to simply analysing local explanations \n",
    "- ***treating the encoded categorical features as a group*** of features that are ***jointly*** perturbed as opposed to being perturbed individually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_set(X, y, fraction, random_state=0):\n",
    "    \"\"\"\n",
    "    Given a set X, associated labels y, splits a fraction y from X.\n",
    "    \"\"\"\n",
    "    _, X_split, _, y_split = train_test_split(X, \n",
    "                                              y, \n",
    "                                              test_size=fraction, \n",
    "                                              random_state=random_state,\n",
    "                                             )\n",
    "    print(f\"Number of records: {X_split.shape[0]}\")\n",
    "    print(f\"Number of class {0}: {len(y_split) - y_split.sum()}\")\n",
    "    print(f\"Number of class {1}: {y_split.sum()}\")\n",
    "    \n",
    "    return X_split, y_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 128\n",
      "Number of class 0: 96\n",
      "Number of class 1: 32\n"
     ]
    }
   ],
   "source": [
    "fraction_explained = 0.05 \n",
    "X_explain, y_explain = split_set(X_test, \n",
    "                                 y_test, \n",
    "                                 fraction_explained, \n",
    "                                 )\n",
    "X_explain_proc = preprocessor.transform(X_explain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only 100 examples for the background dataset to speedup computation\n",
    "start_example_idx = 0\n",
    "stop_example_idx = 100\n",
    "background_data = slice(start_example_idx, stop_example_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploiting explanation model additivity to estimate the effects of categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspired by equation (1), a way to estimate the overall effect of a categorical variable is to treat its encoded levels as individual binary variables and sum the estimated effects for the encoded dimensions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={\n",
       "    'name': 'KernelShap',\n",
       "    'type': ['blackbox'],\n",
       "    'explanations': ['local', 'global'],\n",
       "    'params': {\n",
       "        'groups': None,\n",
       "        'group_names': None,\n",
       "        'weights': None,\n",
       "        'summarise_background': False\n",
       "    }\n",
       "})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_fcn = classifier.predict_proba\n",
    "lr_explainer = KernelShap(pred_fcn, link='logit', feature_names=perm_feat_names)\n",
    "lr_explainer.fit(X_train_proc[background_data, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# passing the logit link function to the explainer ensures the units are consistent ...\n",
    "mean_scores_train = logit(pred_fcn(X_train_proc[background_data, :]).mean(axis=0))\n",
    "# print(mean_scores_train - lr_explainer.expected_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.08786649, -1.08786649])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_explainer.expected_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation = lr_explainer.explain(X_explain_proc, \n",
    "                                   summarise_result=True,\n",
    "                                   cat_vars_start_idx=cat_feat_start,\n",
    "                                   cat_vars_enc_dim=feat_enc_dim,\n",
    "                                  ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now sum the estimate shap values for each dimension to obtain one shap value for each categorical variable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rank_features(shap_values, feat_names):\n",
    "    \"\"\"\n",
    "    Given an NxF array of shap values where N is the number of \n",
    "    instances and F number of features, the function ranks the\n",
    "    shap values according to their average magnitude. \n",
    "    \"\"\"\n",
    "    \n",
    "    avg_mag = np.mean(np.abs(shap_values), axis=0)\n",
    "    srt = np.argsort(avg_mag)[::-1]\n",
    "    rank_values = avg_mag[srt]\n",
    "    rank_names = [feat_names[idx] for idx in srt]\n",
    "    \n",
    "    return rank_values, rank_names\n",
    "\n",
    "\n",
    "\n",
    "def get_ranked_values(explanation):\n",
    "    \"\"\"\n",
    "    Retrives a tuple of (feature_effects, feature_names) for\n",
    "    each class explained. A feature's effect is its average\n",
    "    shap value magnitude across an array of instances.\n",
    "    \"\"\"\n",
    "    \n",
    "    ranked_shap_vals = []\n",
    "    for cls_idx in range(len(explanation.shap_values)):\n",
    "        this_ranking = (\n",
    "            explanation.raw['importances'][str(cls_idx)]['ranked_effect'],\n",
    "            explanation.raw['importances'][str(cls_idx)]['names']\n",
    "                       )\n",
    "        ranked_shap_vals.append(this_ranking)\n",
    "    \n",
    "    return ranked_shap_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranked_combined_shap_vals = get_ranked_values(explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the columns have been permuted by the `preprocessor`, the columns of the instances to be explained have to be permuted before creating the summary plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "['Age', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Workclass', 'Education', 'Marital Status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Country']"
      ],
      "text/plain": [
       "['Age',\n",
       " 'Capital Gain',\n",
       " 'Capital Loss',\n",
       " 'Hours per week',\n",
       " 'Workclass',\n",
       " 'Education',\n",
       " 'Marital Status',\n",
       " 'Occupation',\n",
       " 'Relationship',\n",
       " 'Race',\n",
       " 'Sex',\n",
       " 'Country']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_feat_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def permute_columns(X, feat_names, perm_feat_names):\n",
    "    \"\"\"\n",
    "    Permutes the original dataset so that its columns\n",
    "    (ordered according to feat_names) have the order \n",
    "    of the variables after transformation with the \n",
    "    sklearn preprocessing pipeline (perm_feat_names).\n",
    "    \"\"\"\n",
    "    \n",
    "    perm_X = np.zeros_like(X)\n",
    "    perm = []\n",
    "    for i, feat_name in enumerate(perm_feat_names):\n",
    "        feat_idx = feat_names.index(feat_name)\n",
    "        perm_X[:, i] = X[:, feat_idx]\n",
    "        perm.append(feat_idx)\n",
    "    return perm_X, perm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_X_explain, _ = permute_columns(X_explain, feature_names, perm_feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(explanation.shap_values[0], perm_X_explain, perm_feat_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the aggregated local explanations of this limited set are in partial agreement with the global explanation provided by the model coefficients. The top `3` most important features are determined to be the same. We can see that, high values of the `Capital Gains` decrease the odds of a sample being classified as `class_0` (income <$50k)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping features with KernelShap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='src_3'></a>\n",
    "<a id='f_1'></a>\n",
    "<a id='f_2'></a>\n",
    "<a id='f_3'></a>\n",
    "<a id='f_4'></a>\n",
    "\n",
    "An alternative way to deal with one-hot encoded categorical variables is to group the levels of a categorical variables and treat them as a single variable during the sampling process that generates the training data for the explanation model. Dealing with the categorical variables in this way can help reduce the variance of the shap values estimate <sup>[(1)](#Footnotes) </sup>. Note that this does not *necessarily* result in a runtime saving: by default the algorithm estimates the shap values by creating a training dataset for the weighed regression, which consists of tiling `nsamples` <sup>[(2)](#Footnotes) </sup> copies of the background dataset. By default, this parameter is set to `auto`, which is given by `2*M + 2**11` where `M` is the number of features which can be perturbed. Therefore, because `2*M < 2 ** 11`, one should not expect to see significant time savings when reducing the number of columns. The runtime can be improved by reducing `nsamples` at the cost of a loss in estimation accuracy.<sup> [(3)](#Footnotes)</sup>\n",
    "\n",
    "\n",
    "The following arguments should be passed to the `fit` step in order to perform grouping:\n",
    "\n",
    "- `background_data`: in this case, `X_train_proc`<sup>[(4)](#Footnotes) </sup>\n",
    "- `group_names`: a list containing the feature names\n",
    "- `groups`: for each feature name in `group_name`, `groups` contains a list of column indices in `X_train_proc` which represent that feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_groups(num_feats_names, cat_feats_names, feat_enc_dim):\n",
    "    \"\"\"\n",
    "    Given a list with numerical feat. names, categorical feat. names\n",
    "    and a list specifying the lengths of the encoding for each cat.\n",
    "    varible, the function outputs a list of group names, and a list\n",
    "    of the same len where each entry represents the column indices that\n",
    "    the corresponding categorical feature \n",
    "    \"\"\"\n",
    "    \n",
    "    group_names = num_feats_names + cat_feats_names\n",
    "    groups = []\n",
    "    cat_var_idx = 0\n",
    "    \n",
    "    for name in group_names: \n",
    "        if name in num_feats_names:\n",
    "            groups.append(list(range(len(groups), len(groups) + 1)))\n",
    "        else:\n",
    "            start_idx = groups[-1][-1] + 1 if groups else 0\n",
    "            groups.append(list(range(start_idx, start_idx + feat_enc_dim[cat_var_idx] )))\n",
    "            cat_var_idx += 1\n",
    "    \n",
    "    return group_names, groups\n",
    "            \n",
    "def sparse2ndarray(mat, examples=None):\n",
    "    \"\"\"\n",
    "    Converts a scipy.sparse.csr.csr_matrix to a numpy.ndarray.\n",
    "    If specified, examples is slice object specifying which selects a\n",
    "    number of rows from mat and converts only the respective slice.\n",
    "    \"\"\"\n",
    "    \n",
    "    if examples:\n",
    "        return mat[examples, :].toarray()\n",
    "    \n",
    "    return mat.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_proc_d = sparse2ndarray(X_train_proc, examples=background_data)\n",
    "group_names, groups = make_groups(num_feats_names, cat_feats_names, feat_enc_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having created the groups, we are now ready to instantiate the explainer and explain our set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_explain_proc_d = sparse2ndarray(X_explain_proc)\n",
    "grp_lr_explainer = KernelShap(pred_fcn, link='logit', feature_names=perm_feat_names)\n",
    "grp_lr_explainer.fit(X_train_proc_d, group_names=group_names, groups=groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_explanation = grp_lr_explainer.explain(X_explain_proc_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wl1vWVzvlql6en9gxZif3FFa+vJK68nrqK/2snbWBYE3I70JB1+1Kktp+e5ChqDSitZ4O7AnUR2za0xgzu/175NBaDwYWAQOMMcsb1htjtk9Vn4QQncxlj8EDb0JONjx3BRyzh7P+zpfg+mfA64HJ58OkCQDMXRfklYWNuz862/LQtwE2uSNAF+9seeBgD6e/FeTZueHBiu4NH53kpdDXeFK8e2aQa2cE8Sh4eLyH83dqDECemNO4/yOzLI/MCpDtgaeP8LBnbfgE4eZ4i3MTKh8vay0fXf4Vi95cTlaeFxT4qyLm01ioLasjq4MPRWVKMBOqwwY2rluNMbeluhNCCNFqi9bAsX+GecsBBYN7wgu/hx0GRZetrYdT7oVps2D8GHjuSieI+dPz8LepUF7dWO7MB2DJ9lCYAzc846wPBKm45HG2qTmQs0Yrnp0XHqysrQpv7u/fWZSKDmoAzBp4eYHlzO2dE2TQWm74NOguw7WfBMMCG0/oedRabn/5PX717VwW/bMXnr6FBH+OziY1xQaSM3l3w7xNLHrT+V7q38oE4a1tE6nT0QObmLTWRcDfgYlAOXBjxPabgH2MMeND1k0HpjUESlrrMcCfgV0BL/BNQ3mt9RPAeKALsAy4zRjzrFtVwwDxfK21Be42xtyqtV4M/MEYM8WtY3+3/u2AVcDfjDH/dLcdAEwDTgPuAHoA7wLnGmNi5JaFEB3edVPgu0WN7+etgMufgGk3RZd9bBq84l7u/MpMePwD2Gsk3PLf6LIVNfCX12DM4C33gQEoqK0lZ10ZD3zbhWy/nxs+eoU7DzqWoNcbs3sPftv03X575Yc0VweBkKKh01KshbqApWFexwHzfuG0L5w/mWN+Wk4wO7HsgU3SHJfsgvhOjbVltUBhUvogWi5T59jcBwwHRgNjgKNxgpO4aK37Ah+7r8FAH+CukCKfAjvjBDa3AE9qrUe723Zyf440xhQaY26NUf8Q4B1gMtAdOBu4U2t9QkgxL3CIW98IYCxwabzH0Brl5eWyLMuy3M7L9XUxrvwJBGOXj5jXUlNZFbUusp7qyvA0jAU87qMJrpjxBje//yIPvvo4h8/9pslqfE2cMQ4b4tnSNwt4QiKoQUWNwYfP5yO7vnG4qSDymBOMU6xKzr9FsDAQdQVULFnu3YeT/dlIpkycY9PRA5sbtNZlES8PTqbjj8aY1caYTcA1CdZ7BrDQGHOnMabSGFNnjJnWsNEY85gxZr0xJmCMeR6YBRyQQP2n4GSAnjTG+I0xXwD/BM6LKHetMabCGLMGeBXQCR5HixQVFcmyLMtyOy9n33UWjOzvzH/J8sDAHvDXs2KXP+dgOHRnyPLCoTuTe9GRMHYo/P5o8GXByH7wq3GQnQW7DYffH03emQfBQTsCYJXigaNPZEPXrpy3Ixy2bC5ea7nwi/eZ+sTdjFuygFAKuGMfxYhuRFE4V1Q19K0kR/HgwV7ysqBvATx2WPaWsvX1ddzz9ocU1tTStbKK480c8k/fHrI8+Mb1xTeub3QDW1NVn5R/iy7dStjnjl3IyveSU5JNbo+cmM13G1HSpu02tZxc8qyodHN75BwbrXVvIAdYHLJ6EYkZDPwUa4MbON0EnISTybFAAdAzgfoHxOjTzziZpQYBY0xpyPtKoL0+6UKI9jaiH8x7ML6y+Tnwzo3R6+85y3k15YObAef0dbn7AuDVXuBecuG1lk8H/sQB/Ybz+Urolgsfnuhlp16KMT2DHPtakLqQzMpluyi8nvAT4oVjPVw4Nvb35jOO78VRl90PFkpu3IcuN+8H/3b+9G26byZ1X6xs/vhdKjt5p7ARxw1ixHGN85uq19Xw7B5vhZUJ1gfxJPnKLJG4jh7YxLIOqMMJTn521w2OKFOOE4yE6heyvBg4von6T8HJrBwC/GiMCWqtDY2hbjzJ1GXAERHrhrrrhRCifZ17MDz5IdTUQ0k+vmN24+MhXhZshP5FThYG4MhhHpadr1hZYVlXY+lXoBjdI7ETe/El48j/9UisP0j2kC5h2wpP2Z6yaz4iLHLaiqxB7Xe5dV6PXOevfIY9WDxThp9CZVxgY4wJaK2fBW7WWs8BqgmfHwPwNXCH1npXnMm+vwOGhGyfgjPMdQ3wIOAH9nOHo4rd96WAR2t9Ns48mDfcfUtxgpvhwHJiew74o9b6TOBZYBfgfOCClh63EEK0mN4WvrsXzELYexQM7kU2MLpHdNFeBYpeBa07GWYNKI653tu7kJ7PH0PpsS85KxQUX78Xnm0KKbvgvajyvj37Ra1Lpqw8b9hl3+vmlNFrbIzxuQ4kEwObjp5D+6PWuiLidRRwGc5QzzycBOtUYMun0RgzHbgXZwLvKqA38L+Q7Stx5sxMwAlOVgNXuZufAmYCC4EVOBOUZ4TsWw38EXjOnfNzQ2SnjTGLcDI2FwPrgX/jzAmKcUmDEEK0g5H94bT9YXCvlHYj/5iR9HrnZIqv2oNeb55E19sOwJObHbvsUcPbtW9Bf3gmKa9Xcu6j074yb46NsjbD8mqiLciHQgjRpqZOnQrAxIkTE9531d5PU/dZeAK8+Lo96XrHgW3St3g9t9dbWx6E6SvK5oxvEz+WFkpaxFGmrgn7e9/F3t3ho5uOnrERQgiR4QJLN4W9zz12RLsHNQD7/3UcxYMKKBpQwEEP7tbu7SeDjXhlgoybYyOEECKzZA0uIbC88b4uJZelJqjot2dPTvjg0JS0nSwyx0YIIYRoZ8VX7+nc2wfI3rl34ve7EVuReXNsJGMjhBAireVPHE6/HybhX7yJnL23wZMXezKxECCBjRBCiA4ge0R3skd0T3U3Mk4mDkVJYCOEEEJ0UpkY2MgcGyGEEEJkDAlshBBCCJExJLARQgjRrl5fGOS6TwLMWJ4pd07puCwq7JUJZI6NEEKIdvP6wiBHv+o8muCuLwPs3gc+ONFLgS8zTqodTaYEM6EkYyOEEKLdfLYyPEszczXc9WWQ8s1+Zn9dzobS+hT1rLOS+9gIIYQQLTamhyLy5v0vzgsS+Pdi3i0oYXUxnKCD3HukD4/KjBOtaF8S2AghhGg393wVjFpXVx3k6V69WVGSD8D982ClDfDfiXKKSrZMnOUkQ1FCCCHazYKN0evGdGdLUNPghflwwfuBdupV55WJk4clsBFCCNFuRse4efCrK714YuQO/v1DJuYT0osENh2Q1vp6rfXUVtbxpNb60bbq01baeVtrfXWy2xFCiFS5fd/Yp51gjJNqXjZ8u8byxOwg7y4OUFYT5Ky3Amz/hJ+b/hfgu7WWj5YGCQQlABKNUj6AqbXWwB+AvYEcYDXwFnC3MWZVa+s3xtwR0d50YJox5rbW1h1R767A9cC+QD6wDvgaeMgY82GcfT28LfskhEiRskpYvRGG9wWvN9W9SSvrquMv6w/ALv9uHI4qzLZUuBdN3fy55ebPnW0DiuCDExQB62FEN/AoRY3fct2MID+UWo4cpvjtGA8v/GR5Yk6QUd0Uf97fQ6FPsXiT5dIPA9QG4N79PWR7Fd1yoUd+7OzFplrLqgrYtitkeTIhw5EJxxAupYGN1noCMBW4H7jIGLNCa90XOA/YH3g+lf2LV8hxPABcASwDCoFDgGOAuAKbjDBrMUx+F/p3g6t/Db5mnsIbCMD9b8L8FXDWgbDXdu3STZGAqV/BVAMDu8OKjdC3q/Nvm+tLdc/S02PT4MJ/QZ0fDtwB3r0Rstv2T61/dQWb7/4CvB5Krt0Db4/G+SlV369j5S0G/7Jyuh0zmJ5X7YrKaj45XzpvM3NeWkZRn1zGnjkEb3bT+5Rv9vP+q+uxFiYc3Z3iLvEf3/PzoicPN6WsLvx9RRNXgi8rhxGPWyDA4UMUU4/xcPNnQe772snkvL/UcuP/Amx265u+zFLjD9A1V/GP7y1Vfmf9TouDBIBsD0wYBHfv52WHno0n/ncXOffgqQ3A7n3h45O85GQ1HRh8ucry+Jwgw0oUV2iVloFQpgw/hUp1xuZh4FljzDUNK9wsza0N77XWJwPXAUOASuB14EpjTKW7fTHwOE4QsTMwD7jAGPOVu/0mYB9jzHit9d9xMip7aq2vBVYYY0ZqrQ8G7gBGAH7gA+BSY8zaOI9jMjDFGBM6jFQOvOS+4j2W6bjZJK31YGARcKa7zwDgc+CstshkJUVZJRx4I2yocN6Xbob7z936Pne9An941lme8gnMexAG9EhuP0X8vpgPv74bghEno1UbYfL5qelTOlu+Dn47GRqGRj6aAzPmwkE7tmkza498gbpv1gBQ+9Uq+n58GgD+jbXMPeB1Am5EUPXVGmyNn94377nV+qrL6nj1/C+pLXfO8DWb69nniqa/ZDxyz3J+me+kXn6ZX8VVdwyJu+/18cc1LfL2IsvMVfDawvDhqc0RQdKLP0F5fXiZhtxQfRDeWgRfrQ7w8yQvRe7NA09+wwlqAGaugg+WWo4YGjswWFVhOfi/ATcYs5TXKW7ZJ/2yd5k4iJeyOTZa6xHAtsCzzRTdBJwKdMEJSvbFGboK9TvgMqAb8CLwlta6OLIiY8zFwAzgVmNMoTFmpLupFrgY6AnsCPTDySLFexzDgOfiKB7PsUQ6CdgP6A8UALfE06/WKC8vb9nysnWNQQ3g/+bnZvetNwsaG66qhQWrWtcHWW7b5VlLooMagO8Xp75vabhc9c3CxqCmQZ8ubdqWtZa67xu/c9V9v3ZLmbplFVuCGoAgHmq+X9dsneWrqrcENQDrfyrfavnli2u2vF+xpDbu/vt8PvbbJrmnHQ/QPc8yf8PWT9nVcVxwVVoNC9ZUAlAfsJTVhm619C1wgppYx/tzWXiG6etV/qgy8S6LxKRy8nBP9+eKrRUyxrxtjPnBGBM0xizEyfIcHFHsMWPM18aYOuBuoBo4Kt6OGGM+NcZ8ZYzxG2NWA3+O0Ubcx6G1/pXWukxrvUlrveUvQJzHEulmY8w6Y8xmnCBQx3tcLVVUVNSy5ZH9YNy2W9ZnnXlgs/tmn3EgeNyP4YjG/VvcB1lu2+XDxkIP9ztCaBr9tP1S37c0XM4/YEz4lIWuBTB6QJu2pZSi4LTtt6wrPH37LWVyt+tC/i6NGc8sFaTLKSObrbP7sCJ6buf+OysYfljfrZYft2/Jlvfj9i2Ou/91dXVMGJTcE891uyuGd1XkZTc9xFKUDSeNjL09L2QcY4++MKZ/AQDZXsXxIxr3mTBIMba38z7W8Y7tBdu7V4B5FJyxY3ZUmXiXkykTr4pK5VBUqfuzPzC3qULu/JUbge1wJhd7gcghosUNC8YYq7VeCmwTb0fcib93ADvhTPxVOHNk4rHO/bkNzjAYxpjXgS5a631wMkSJHEuk0GGnSqB9Pu0t4cuG6bfCu99C/+6w2/Dm9zl2D/jmHvh5jZOuL8pLfj9F/Ab2hO/vhc/mwXb9YeFq6NMF9hjZ/L6dUXE+vHAVXPIoFOTAlMuT0kyPJ46k4ORRKK+HvEMah4E8Pi+jPjmaja8uwr+inJIJA8gb26vZ+rw+D8c8uhvLvlhPYZ9ceo8u2Wr5kyf1YafdnD9Fo3YqSKjvp70ZpDWjUV4FXXJgfWPSCJ/XWXfODorb9nWGe/470cN57wZZVRm+f98C+OZML73z4YzRlg018MXKIC8vgL6F8PThXspqYVWl5bDB4fNi/jPRw9u/WHKyYPygrYdnBT7FZ6d6mbbEMqSkMQhKP+nar5ZLWWBjjPlJa70QOAWYFquM1toHvApcDTxujKnWWl8M/D6i6OCQfRQwEFjeRNOx/k89jzOEdYIxZrPW+iicycDx+An4BTi5qeNw+xXvsXRs+TlwzB6J7bPTEOcl0lO/bnD8Xs7yDoNS25eO4Lg9nVcSKY8i//BhMbd5C7LpcdqIhOv05Wcx7KDe8bWvFKN3jve7X7glm+Mvq4B79ld4lKLYB5vqYP9tFLv2UVw0zc/k76BnPkw/0cOoHuGBxhFDPay8wMPMVZYZy4KU10HfQsVZ2zdmcw4d4vw8ZZSH+6Ny59EnfI9SHDks/kCgOEdx7Ij0DhwyJUsTKtWThy8Epmqt1wB/N8as1Fr3Bs7BmTj7Bk5mY6MbCIzGmQsT6Ryt9SvAbJyrkvKBN5toczXO3J5QxTjzX8q11gOBa+M9AGYeBOIAACAASURBVDdDdBHwmtZ6PfB3nKAqD9g9pKgvzmMRQoiM1TMPllU0Xw6cia1jesCEIdHZkYfGZ/HgwbbZ50nt3lexe9/0m7QrkielN+gzxrwP7AOMBmZrrcuBT4FewHRjTAVwAfBnrXUF8BCxJxv/C+dS6404k22PNMZsaqLZv+HcPqdMa/2Du+63OJeYlwMvAy8keBzvuMcxAvgGqAB+wLk3z0FumXiPRQghMtbpo6LX9SuA4U2Mfs1Z33Rd8pDM1rMRr0ygrO3Yh+Je7v0HY8yUVPclg3TsD4UQIu1MneqM7n/R5XDumBm+rUsOEVccOfKznPkwI7t1+gAmab+AJeqOsL/3g+z1Hf6XnfGPVBBCCJE+PlwavW5wxM05FHDZLvDDbySoSbZMvCpKAhshhBDtpk+Mi6ju2Fcx1B2KGtcHvj/Ly30HZTG4JDNOtKJ9pXrycKsZYwanug9CCCHic/9BXt5aFKDOvUFeUTbs1d/Dz5MUdQGLzyvBTPvKvN93hw9shBBCdBwDixVLf+vhyo+CVPrh6nFeSnKck6sENe0vEydUSmAjhBCiXfUu8PDMUTITIh1kyryaUPLJEkIIIUTGkIyNEEII0UllYsZGAhshhBCik5LARgghhBAZIxMnD8scGyGEEGlp4Yp6Xv1fNUvX+lPdFdGBSMZGCCFE2pmzqJ5Jf9tAvR/ycxRPX9ONwX3klNX2Mm8oSjI2Qggh0s57X1dT7yZqqmotn/0Y42FSotXkkQpCCCFEGyqttJw/tY5TX6zjh7XBLesXrw4ffqqrb91sEP/rs6nW91A96nbq/vJBq+rKJJkY2EheTwghRItt/nodtUsr6TqhH1mF2Qnvf8qLtXywyAlaPl4SYOkVufy4pJ7vf6kPKzdyQOJ1NwjOXU3dsY9BwAmc/Fe9hqd3EVln7NbiOkX6koyNEEKIFln11ALMuKnMPvZDvtnvLYK1gYT2r663zFjSmIlZWQ5Pf1jNb+7ZSGVNeNndtvO1uJ/BBaVbgpoGgQ9/anF9mcRGvDKBBDZCCCHCvfYl9DgLup8JQ86HvJPh909GFVsxed6Ws2HFtxuo/GFjQs3MXmOpC4k3eubDO19WR5XL8oDX0/JhEjWke9Q6W59YEJapZChKtJjW+h+A3xhzcar7IoQQTVpaCif9BWrdOS4bKpyff32d8rE78fPTa9n01TpsfYBgdXhw4HGHomwgSNmt/6Pyxfl4uuXiKfIx5OdVVI7JJ3hwPeWeLE57qY4fSsOzKB4F8zdFn5j8QSirCNKlsGXfxT1DowMb/MHodSIjpG1go7XeE/gTsCdOP+cBDxhjnkppx+KgtV4M/MEYM6VhnTHmd6nrkRBCxKG8Gva6rjGoCeEni2/On02gMnamQxGk7vPl5ORCxTM/Unbz/8K2FwAF86tZX/geE/c8mJkrogc+KioCrPZm0T3bUp6bRVF1PXluAHLdY5uYfFnXFh2WKsiJWhd8d16L6so0mZKlCZWWQ1Fa60OAj4DPgaFAT+Bu4D6t9c2p7JsQQmSkskrQV8GKDTE3l9KryaAGLH3YSNnZU1m+3SNUTJnTZDPV05cya3Xs2RyVeFAofhzQhfosz5agBuCHxXVxH0qk4JIYx1RWja1qeZ2ZIhPn2KRrxuYh4DljTGgQ81+tdT7wqNb6CWAJMAm4BBgEbALuNsb8HUBrfSxwPbAtUAM8Zoy5QWt9Nk42ZduGirXWT+IME52ntR4MLHLrvhroDUwHJhlj1rrlLwMuAPoDG4Fn3DoDWuupwEC3n/8APjPGHBLahlvHIOABYG+gGngJuM4YU+1ut8BFwG+A7YAfgLONMfI1QwjR9j6YBT+tbHJzDrU4p75Y3/AVAbyAH1vtx7+ifKtN+bKguokYaU2XPAB6bg6fPVyY2/LMQuCl76JXehUqv+UTkjOFZGzagdZ6BE4wMiXG5mdx/ldNAH4H3IQTYHQBxgIz3ToOB55yt/cARgBvJ9iVM4H9gAFAMKI/y4HDgWLgaOAc4DwAY8xEYClwnjGm0BhzSIxjzALeBFbjBGV74AQ4f4koejZwnHsMy4AHEzyGFikvL5dlWZblzra8bR+savok14319GR1E1stWTQOX3n6FTRZT/6EIew7qIl2lMK6k4Rrs71hm4rzGvMJiR5jTc/c6LZ6Fqb+dx7nskiMsja9kk9a672BT4FRsbITWus1OJmO04CHjDEPxSjzFvCDMeaqGNvOJr6MzXhjzAfu9m2BBUB/Y0zUVxqt9V+AgcaYE933i4mYYxPRxl44Q23djDGV7vZDgVeBfGOMdTM2JxpjXnC3HwlMMca0bJA5Men1oRBCtI/LH4P732xy8xdFe1Aa7IfHH3CGiUKyLvneWvrvWUzh+IHkjh/E6n2eadyoIJCjKB9byM4fnU9Z0Mt5r9cxdX6Q+tA5vFkKcp2BhCx/kH4bq/AGLd0q6zh8XA63/qZLiw7Lbq6muuSasHXq8NHkvdVhpj4mLa0yWz0Q9vd+R3tph0/hpONQVKn7sz/OhOEttNY+nOxFKTAYaOpGBIOBV1rZj8UxlrcBVmqtTwGuxJn/kwX4gC8SqHsAUNoQ1Lh+BnJx5hOtddetCtleCRQl0IYQQiTmz2fC/JXwzrdRm+o8Wfy80/aUlwXZ4ZTBjD12ADNHNf6ZrckpoP+0k/DkOJmW4ss1m+83ePsW0uftE3l3yUwAxuZk0RV46aQcflgbZMzkWoINp9aAxRMIEvQo/F7Fsu4FDC51rsrafVTLh41UcZ5zyVWw8RyuusbI4nRCmfgtNu2GonAyI78Ap8bYdjLOv8P7OMHG8Cbq2Nq2cpwJ+qH6xSg3OMbycq31AJxhqduAvsaYEpw5QaFRbnPXES4DerpzhhoMxZkLVBp7FyGESDJfNrz9R7jwsOhN2/fntGmHMOmbI9nn+h3JG1pEVpfGYCNY5adsxpot77v/bTyDa65i4IqL8Y3pFbO57Xt56B3619jCbv1gxMrN7LJoA2MXb6BbpTPBt7oVj4qym6vDghoAVZLfROnORe5j0w7cYZiLgVe11ouAh3Em1x4J3IczQXiR1voh4Hqt9bc4c2u6AUOMMV/hBBrPa60/wgmC8oExxphPge+AXlrro4C3cObI7Ef0nJ4/aq3nuG3fDUwzxqzUWo/CCQhLgXqt9R7AGcDckH1X03RgBfAlsBD4q9b6/3DmCN0KPGGMycQAWgjRkfx9EhyyM1TWQJcCKN0Mv94NcrJpmPni8Xnp+5ttWfa3HwFQXkVO//BgQfm8NGfvgR5e/NH5LliQDa8dm8VbY7ow+dF1BEKGulr1ZO98HxT6oKLxKijvwSNaXp9Ia+mYscEY8zZwME7AsRhYB9wA/N4Yc4Nb7GHgTuAxYDPwDTDO3f9N4FzgDmADMB841N32M3AZ8C9322E4VyRFmgLMwMmu+HCCF4wxc3Hur/MaUAZcCzwXse9twOla641a66hJy8YYP3AUztDWUpxAZybw+7h+QUIIkUxKwdG7wan7wRG7wlkHQkn0hOCht+/KNpeMouv4foyesh8FoxKfAzP5SB9n7uTl0GEe3jjVR68CD2eN8USNkQzp03yQ1BSV5SXno0tRI3pBryKyrp9A1nE7t7i+TJKJGZu0mzycaiGThwcYY5anuDupIh8KIUSbmjp1KgATJ06Mq/wxf1rHstLGlM2DF3dhz9HRN9rrJJIWcXyr/h72936svbjDRzdpNxQlhBBCRD4aqqAV97ERTcuULE2otByKEkII0blt27/xe3eWF7bpKd/DRXzkkxLBGLOYJKb9hBBCNO+qE4vwB6B0U4DfHFpAtyL5Hp4cmXe6k8BGCCFE2ulR4uWvv2vZDflE/DJxKEoCGyGEEKKTysQrRSS3J4QQQoiMIRkbIYQQopOSoSghhBBCZAwZihJCCCHShD9oMastK8oz8fTcPoKosFcmkIyNEEKIDscftBz2YpAPllp8Hnj11x4OHyrf1YVkbIQQQnRA362FD5Y6mZq6IPzu/WCKe9QxZeKzoiSwEUII0baumwKjLoGzH4Ta+qQ0UewLH35aWg7V9TIklSgb8coEMhQlhBCi7Zx4D7zwubM8bwUU58IDk9q8mRJf9GnYK1/VE5YpWZpQ8jEQQgjRNp6f0RjUNHj4XdhY0eZN3fR5dGBTH4hRUHQ6EtgIIYRoG09Pj14XCMKHs9u8qY+WRq8rrW7zZjKezLERQgghYllbBp/8GHvbvBVt2tSaSosnxjm4KFsmECdK5tiIhGithwJ3A/sChcBGwAAnGWPqUtk3IYRoM7e9AH98runtOdlt1tRF0wI8/F3sU/AXqxRHDmuzpjqFTMnShJLAJrneAt4DRgKbgf7AUWTic+KFEJ3L1K/gpL9CdRzf0boXAlWtbvLcd/w8Pqfp7bPXBTlymAxEdHYS2CSJ1ro7TkBzrDFmk7t6OfCPkDK/Bv4IDANWAbcZY55xt90KHA3sboyp1lpvB8wEjjfGvN9+RyKEEBGsjT+oAcjPobWBzQdLglsNagBWlLeqiU4pEzM2EtomiTFmPfAD8KjW+kyt9Wit9ZZPkNZ6AvAYcDnQDTgL+LvWej+3yE3AeuAhrXU+8CJwX3sENeXl5bIsy7Isy00vW5vYJUi5zlCUz+drcbsbK5qfGVxR03jPnLT5XbXBcjKl2xwbpdR2Sqk/KqUeCnk/JqE6rE2HQ8lMWusewJXAYcAOQBnwIHAbMBX40hhzS0j5B4E8Y8x57vs+wLfAapwg5xBjTHvMjpMPhRBi6x7/AM57KL6/FtNvYermRQBMnDixRc0FreW0N4M8P6/pBq8eB3fvn5EDEUlLq3ykngj7hR5of5OyFI5S6gTgIeBl4FRrbbFSSgN3WWvHx12PBDbtw826nAg8ApwP/B8wGAi9LacXmGGMOSJkv/uBS4GDjDEftVN35UMhhGheMAj1fue0++u74e1vY5ebeh1T7Wqg5YFNA3/Q+fN00H8CzIi42Or2veH6PSWwSUSaBTZzgZOttd8rpTZaa7sqpbKBldbanvHWk5GfgHRkjKkCntRaXwLsDCwBnjTG3NPUPlrr/YHfAI/jDEmNM8ZUtkuHhRCiOR4P5LjDS2/cAL4TIBDje1F1HeS2TZNZ7nXegRi561NHy+yKRKXZt9hewCx32Yb8TKibEtgkida6K3A18AwwH+cf5micIam7gDdwAp0vgM9wsjU7AsoYY7TWvYHngMuAp4B3gMnAme18KEII0TyPB07aB56dEb1tl2Hw44Y2ba7KH71ucIkENolKs8nDXwNnAE+HrDsZ+DKRSuRTkDx1ONHny8AGoBT4A3CpMeYFY8x7wCTgHmAdzlVRfwMKtdYenIDoPWPME+68mtOB8Vrrc9v/UIQQIg5PXQrbdAtfd8o+MKxPmze1Z7/oE/KqijTLP3QAaXbn4UuB25RSHwMFSql3gVuBKxKpRObYiFjkQyGEaJmqWjjwRuduw/uPhlevBY+HqVOnAq2fY9NgTmmAHZ8K/1O1+gIvvQtSfnJOhqQd1DQV/kscb89K6S9QKZWPc7+3QcAy4A1rbUIPG5OhKCGEEG0nPwdm3p30ZiweoPGS88HFZGpQk1Tp9hAKa20V8N/W1CGBjRBCiA5nhx5w6ijFs3MtRT548nBvqrvUIdlYD91KEaXUDJoYMbDW7hdrfSwS2AghhOhwlFI8c6SXO/e1dMmB4pz0OUF3JDa9fm2PRrzvA5wLTEmkEglshBBCdFgDi9PrzCxazlr7VOQ6pdRLwBPALdF7xCaBjRBCCNFJpdNQVBNWAAk9UkECGyGEEKKTsml00xel1DkRq/KBY4EvEqlHAhshhBCik7LetMrYnBHxvhLnBrZ/S6QSCWyEEEIIkXLW2gPboh4JbIQQQrSLNfV5/LjOMrpHWmUJOrVgiufYKKWGxlPOWvtLvHVKYCOEEKLF1q7389OiOrYd5KNPz6ZPKW9uHMg/146GXwJMGqP41yFy35l0kAZzbBbi3LtmaxGWxXmeYlwksBFCCNEiK1bXc/mta6mutfiyFffe0IvB22RHlbPW8ujaUTScux6ZZbl17yC9C1J/Vu3sUn1VlLVtH1rJp0oIIUSLvP+/SqprnRvF1tVbXnhrc8xyqyshEHG6+WipPJJOJIdkbIQQQrTI17Orw94vWVkfs1zXXEvkaMMXqywnj0pi50Rc0unOw0qpLOBCYH+gByEfmEQeqSAZGyGEEC2yel0g7H1llft+6lcw/ELY+Ur49he+W6uInEKxua7t+2PrA80XEmGsR4W9UuxvwPnAJ8CuwEtAL+DDRCqRwEYIIUSL+P3h7wNBoN4Px98DC1fD94vhuD+zrDx62KlXXuLt2aBl06JyajeFR0W2LsCaI//DUt/drBj1T/zLYw+JiWhBFf5KsWOBw6219wN+9+evgYQuA++wgY3W+kmtdeQDsxKt422t9dVt1aettDNYa2211tukui9CiAxXWw9vfwPfLUp6UzYiXvEHcAKbupCIZ0kpA4oahqIa5UfPMd6qYMDy/vmf8eKE93lu77dZ9eW6Ldsqn/uBmrd+dvowbz0rRv+LwNrKxBoQ6SAfWOYuVyul8q2184CxiVSSsjk2WuvpwJ5APRAAFgG3G2NeSFJ7FtjXGPNpwzpjzOHJaKsl0qkvQogOyh+A3a6GWUuckZ/J58P5hya1uVDBoI2x0nLH59FX8y5LMKmyfs5Gln20BoBATYD//eEbjn/vEABqv1wZXri8jvLJ39DlT/sm1kgnlAbDT6HmAuOALwED3KSU2ozzvKi4pTpjc6sxphDoDjwJPKu13ja1XRJCiA7qyQ+doAacBMl1U9q8ibXr/Tz23zKef2MznogzSPdVq+CVmWHr6j0e3lwUPRRVHnuecZNWziwN339ZY0bGvyY6O1M/d13UOhHNqvBXil0GNKT7rgR2ASYCv02kkrS4KsoY49daP4IzcWhnYKHWOh/nMeXHASU4EdzFxpiFserQWt8BnIwz0WgN8KAx5j532/dusfe01kHgeWPMeW7WaJox5ja33BjgPpy010bgceBOY0xAaz0YJ6t0JnAdMAD4HDjLGLPK3f9S4Aqc2dybgaeMMdeHdPNArXVT+27pS0hbk4Crgd7AdGCSMWZtYr9dIUSnMfmd8PdlbTscEwhYrrunlDXupOHIwGbommVwYXjS3WMtfTZtZGVJt7D1eQnen+/nqUvD3ttg43L9wo1R5T298xNroJOyKvXRTANr7VchywuA8S2pJ9UZGwC01j7gAvftT+7PR4DtgD2APsBM4A2tdVMjsz8C+wBFOAHBnVrrQwGMMTu5ZQ4xxhQaY86L0YcS4H3gI7e9I4FzcKLGUCcB+wH9gQKc4Aut9QjgLuAoY0wRsD3wejz7bsWZbvkBQBBo+69fMZSXl8uyLMtyR1yuqiWU9Xq2Xj7B5fLK4JagBsKDC4D8uuqoPnitZURpxFARsLGqMWUTVx+WVYXtb4N2SxlVG301VH1BY+SU8n+XVi53Fkqp75VSVymlBrSmnlQHNjdorcuAauA24DxjzCytdQ/gVOBCY8waY0wdcDPQF9g9VkXGmCnGmJXGGGuM+RB4Ezg4gb4cCdQBtxljao0xc4G7gcgg6GZjzDpjzGbgWUC76/04g8jba60LjTFlxpjIR603tW9TbjbGrHbLXwVM0Fr3S+CYWqSoqEiWZVmWO+Lyb8L/5KkBPdq0/pIiD2O2y9myPjJj06OiDELaBGdE7Jv+0Y8DyvE1fkeNpw/ZeREDDBb81X6KiorI3iG8TYCs9Y1XTqX836WVy8mUZldF3YQzx2auUupjpdT5SqluzewTJdWBze3GmC44Qzdv0XhJ1xD35yytdZkb/GwAsnGyF1G01pdqrWdrrTe65ScCPRPoywBgiTEmdDD45xjtrQpZrsTJEGGM+QU4DSdbtFJr/anW+pB49t2KxTGWm7yySgjRyV31a9hrpLOsgFtPadPqlVLcfFkPrjm/G7dcER1MfL39rvDf/wtf6VVszoseFqpIcI7NzhdsF7UuUOekjIouif6OGNxYk1gDnVQ63cfGWvuKtfZEnCTG48AxwDKlVOTox1alyxybjVrr84CftdZH48w/ARhujCndyq4AaK33xsmuHAzMdOfEvEj4NPzm7t+9DBiktVYhwc1QGi89i+c4XgZedofWfge8prXuHu/+MQzGCa4algGWt6I+IUQmUwpm3A5f/wzdi2BonzZvIjtbse+42PNX1g4cCLnhQ1HK4+W44fDSgvCyNRH3wGnO6DOHobIVX9zyPcF6y9hLtiOnxOf0aXCXqPK5Bw9OrIFOKg0mDEex1pYrpZ4FygAfcEQi+6dFYANgjNmgtb4XuAPYEWeo5mGt9eXGmBVa6y44GZ33jTEVEbsX41wyXgpYrfWRwOFA6Cy21cBw4FNiexNn4vD1Wut7cLJG1wD/jKf/WuuR7j6f4AytbcIJpoJb268Zf9Raz3HruxtncnH0YLUQQjTweGDc8HZpKmreqQLKwx+zQL6PE0cqXloQ/t1y2+hYpFmjThnK8GMGEagLkFPsa2y2yAc+D7gZHNU1l6JJCd36RKQBpZQCDsKZinIMsAQnFjgrkXpSPRQV6X6cFNSZOEM684HpWutyYDZwArEzL+8CT+NcObUOOB54JaLMDcAt7lBVVLBijNkEHIIzC3tNSJ33xtl3H3AjznBTGXApcJwxpjX50CnADJyskQ84oxV1CSFEm8rNCX8fDFoY2Q9yQ67xOHo3duwZnRboV9iyNrNyvWFBDYC3ax49XzwOn+5L3hHD6DfrPFR63Z8lbVmlwl4pthL4h/tzb2vtWGvtPdbauEdOAJSNvHWkSLmQy70HGGNSMfQkHwohRLP+7/bVzF/UOKY0qH8WD93cB2b8CA+9DQN7wk0n8cKybE6cGp68vnkvuHGvtBk0SHdJizj+PeiFsL/3Zyw5IWXRjVJqN2vtl62tRz5VQgghWmSHkbnMX9Q4M2D0tm4mZd/RzstVkhM9Iu9NeXJAQNrdx6bVQQ2k31CUEEKIDuKIAwopKnBOjAV5imMPLY5ZLtao0PBu6XNCFZlFMjZpyBizmCSmHoUQoi307pHFP27rw+Ll9QzeJpuSoti3Ez5ggGL7vA38UO3ckmTnnnD0MPlenQ7S8aqo1pLARgghRIuVFHnZadTWn4+Q5VHcMWAmq+vyOPCggxjWVeFJoyGQziyYgf8OEjILIYRIOqWgb041w7t5JKhJI2n2EEyUUhOUUo8ppaa677VS6qBE6pDARgghhBApp5S6BJgMLMB5TiI0PnIpbhLYCCGEEJ1Umt3H5nJgvLX2LhpvbjsPGJlIJTLHRgghhOik0iCYCVVE42OMGu6vk43zgOq4ScZGCCGE6KTSbI7NJ8C1EesuBT5KpBLJ2AghhBAiHVwCTFVKTQKKlFLzgXLgqEQqkcBGCCGE6KRsej1Taw0wzn0NwhmW+tJam9DDpCWwEUIIkRTrX/qFzZ+tgX4F2CkV2K4eTP1Kykrr2HFCT5TPy1fvb6BLz2y23aML70yvpLDAw6/GF5GdnVYn3IyVLnNslFJeoALo4j5aocWPV5DARgghRJvb8Npi5h8/jYBSbCzMA6Uo7VvMnIeXAPDNW2upLsqnqjwAQPXLZSxWuQAsX+XnsnO6pazvnUm6ZGystQGl1E9Ad5yne7eYBDZCCCHaXPnMtQD4vcq5Ox9QXZCzZXtNdZAqAlveBzbXQYkT2CxYVNuOPRVp5BngDaXU/cByGq+Mwlr7YbyVSGAjhBAifu99Bx/Oxq7ZjK0KUlfhwb85QDArB7vvcAIlxQQr6/FuqsHjsXj8QVQgSF2Ol/zyKiqKcsmq96MKsggUZ7Gh2qk2qyALTzDIpqwsVuVn8/J3tew+JJvbP6ln9npLjfLSvwh26eNh0hgPfQvTI9PQ4aXJUJTrAvfnTRHrLTA03kqUtbb5UqKzkQ+FECLaX16Fq54OWZFFgBzK6U0teZRRQsNdROrwsp5CQBFUsLpvAXU+L5XFeSigKsfH/KEDsR73riPWsjQvl1X5TtamQsGKHoXUKw/keBpPwAp65ivmnuOle15anZSTKWkHOnnMG2F/7y+YdVSH/6XKfWyEEELE55WZUau81BEgm3qyCT2lBFE0nI89FnJqAwSyvFvO0BUF+Y1BDYBS+EPmexRaqA8AIUNZDUqr4avV8v2rLaTZnYfbhAQ2Saa1vkFrbbXWZ6W6L0IIscXaMvhsHlTWNK5708BzMyDQOPeF2nr4fD6sWA87DQaclG4QL0GyCJCNBz9Z+AlN9qqQZQvUZiu89X6U3w9BS25NLYSOGFhL6DW9W3oVjA5gcjywfffMOAmLRkqpZUqppbFeidQjc2ySSGvtASYBG4DfAk+ltkdCCAF8+wsceCNsqoLt+sPnd8Hp98GbXzvbb3oe5j7oBDUH3AhfLoCcbPA2hCt5KDxYwJJFHpvxk0UuVdSRSxBFHTko/NT4fKzpV0xFcQ6eQJCiTdVU5WahCgvoVlFFRY6PgNeDN2ipx7l3/qZsL+t82VAXcAIbayHHC17nu3i9hVWVMKA4Jb+9jGJVWuU3To943xe4DHg+kUoksEmuQ4H+wK+BN7TWOxhj5gBorUcAjwBjgUXA48B9xhjlbs8CrgbOBnoBPwCXGWNMex+EECLDPPGhE9QAzFsBb33dGNQA/LQKfloJKzY4QQ04QQ7gnDack6ECPATJwk/w/9u78zi75vvx46/PvbNnsskqkkgitqCUN7XETu3UUlstoYqWqlJfqkptVXu1qupXBClqK2JpS4jS2t6IncgmhKySyWQms937+f3xOTO5d/ZJ5s6dufN+Ph73MWf9nPc598497/v5fM45xEhQQJwk8Wh6XTyfpUNLWdUvXA2VjMeoy88jL+kpLa+gYkBfiurqqMrPp6qwgGHVNczs1yckNfWSHmqSUJSXNun2dxPssL6dwtZVd7ncG8B7mH3QQQAAIABJREFU/1Ljac656cA/gVvaW063StVy0OnAs6r6NPAecAY0JC1TgXeBYcDhhJqdVJcDhwH7E67rvwv4p4gMzHTQ5eXlNmzDNpzDw1UjBjQMe+dgo+HQt7hhGjFHeXEcRg+GvDjp0m8CG2ptXFrTE0AdcWI+SV5taNZyUc1LPJEkEXPU5ackKtHJtSIeoyDZwk1mG7VIbTzArdW+98ThTOoBfWyqgbEdWcGuisoQERkBfA58X1UfF5FzCMnKCGA7YBowQFVXR8v/EPirqjoRccBK4CBV/U9Kme8D16rqlAyHbx8KY3JZIgFXPAxvz4Gjd4YT9wCdBcfcGGpmbjoFjt4lLPv463DXNNhkBOTn4W9+EqrBU4jPyydJHtWUUJ0oYrXrQ01BCRWukLKqQjyeqvw8lg3qQzzp8UBZ/2IqB5TggfLSEiqKS1hVWMCXpX1YUVhA0jk+Ky5geUGc6lgcCuKhGco5yHPg4NtDHW+cGCevG9U2ZFjGdvSP2/0r7fv+p2/tl7WD6py7otGkEuBA4D3v/bHtLcfq8TLnh4S+NU9F41OA64BjgNXA4vqkJvJ5yvBgoBSYKiKpH7p8YGTGIjbG9A7xOFze6Dwh42H2n5su+73vhBfAX/6Fq64DwFEHV/yA+C+PJJ/whVVvwY3v8flv3iUZi+EdxKMOwA4orqqlMhrus7oK52J8Mng9VhQWAKEZYbOqGk46cTCnPlkH+SmXetd5iMN7S13mzvS9Tfc6kKMajVcANwH3daQQS2wyIOo0/ENgAPCliNTPihOaoy4AhohIcUpyMzqliKWEN3QfVX2za6I2xpg2pDZXNTceiffNx9X/JPOEzr9RcpLa3OEbLgdPryTOz4P+RS2fcQviEO89tTUZ1c2an37pvV/YeKJzbjjQZHpLrI9NZuxPyDx3BrZJeR0M7AiUAfOBa0SkSETGAufWr6yqntBR6gYR2RhAREpFZL+oicsYY7resRPhrANg3DCYtCec8d1mFxt26qaM3m998vOgoG+cosI4LulxeZ5Vo/rjHSTzYtSWFpPIz2NjVtPP11FaV0e/ZB0/PKgPh0+Ic95OeYwu8fQtiPKiOJQWOR46xE5dncXHXNory2a2MP2jjhRifWwyQESeAOpU9chm5v0PeBu4FbiDcFXUHEJT1VWqWhgtlwecA5xGaH6qAF4DfqqqX2Z4F+xDYYzpFB9fMYPPbvywYXzJsD5UF4erng6+bQdG7zIEgJN+voAVK0PH4UED49x9g/2GS5GxjOOW7zyX9n3/s9f3zWYfm3Lvfd9G0/oBc7z3g9tbjjVFZYCqHtbKvJ1TRnerHxCRM0jpZ6OqdYS2xZsyEaMxxnSFRHUibdylnEYTNWvm1daumVFTa7+tukp3aIpyzn1B+EFd3MzN+AYBD3SkPEtsskREJgJfE2prtiLcsybTVzsZY0yXGn/25iyZ9jXlH5fht8mjZL0BVM2pYNw+w9lwt2ENy515wkD+OHk5OPjxCRm/q4WJdIfEhnBjPgc8A5yYMt0Di7z3n3akMEtssmcUcD/hCqglwMPANVmNyBhjOlnR+iXs+drBPPmPJ4nlOY49ZA8StUni+en9ZHbfsQ8TdygJN/3Lfl+PXqM7JDb1N+Zzzg323leua3mW2GSJqj5AB6vXjDGmp3J5a06gjZOahumW0PRq3vtK59w2wK6EH/0uZd6l7S3HupYbY4wxvVR3uvOwc+504L/AXsCFhG4a5wPjO1KOJTbGGGNML9WdEhtCX9P9vfeHA6ujv0cBta2vls4SG2OMMaaX6maJzVDv/cvRcNI5F/PePwsc0pFCrI+NMcYYY7qDL51zY7z38wg36zvMObcUqOlIIZbYGGOMMb1UN7jbcKrrgM2BecAVwCNAAeFmte1miY0xxpgu5RNJZl7/AStmfMMGR41h5FFjsh1Sr9UNmp8aeO8npww/65wbCBR471d1pBxLbIwxxnSpDy5+m7m3h3uuLXp2AaXj+jJg20FZjqp36k6JDYBzbhBwILC+9/4659xg59wA7327HyVknYeNMcZ0qfn3zU4bXzFjWZYiMd2Jc2534FPgB8Cvo8kbA3/uSDmW2BhjjOlSicq6hmEPlG7eP3vB9HLd7Kqo3wPHeO/3B+o/JK8DO3SkEEtsjDHGdCmfcv50QMWSDl30YjpRN0tsxnjvp0XD9U9CraGD3WYssTHGGNO1kumjX72+JDtxmO7mI+fcfo2m7QO835FCrPOwMcaYLvPVk/NJrRfwQGEfOxVlSzeopUl1PvCUc+5poNg59xfCzfkO60ghVmPTjYnIPBE5IdtxGGNMZ/BLE8w4bjqxZHqVzYDhRTz587e5fa9pPHvxeyTqki2UkO69hUne+qp9y5rmdaemKO/9a8C3gA+Bu4C5wA7e+zc7Uk6babKITAeeV9Wr2jPdGGNM71U99WNWX/8ybkAxpTccQNnLi6j6YBkFAyspuXoltT5GXSxOTZ4nGY/jgI8e+Jx5K0KXis+eW8gG2w4gf/x6fPzOKsZtVsI2O/Xjky9qeer1KkYPjXPUxGKu/k8dl74Q+pcevEmMY7bN56FPkhTE4eQtYxyykf1ubw/fDSpsnHPDvfcLAbz3XxFu1LfWum39n4jkq2qHHnyVS9s3xpiMWr4KTvg9fDAfJu0Flx8Llz0Ik1+ALUfDlHNhYGlY9nePwfWPQ1klJKIaki1Gwau/w3+ykJrj76ZiXi3JRGxNl09g8dSZLGEQnhh9KKQmP49VxUVhpvfU5DmqSvKom1UWthWPUVlUyKP3LWVFfjngePHpb2Bcf+YuS/JJUSHL4zF++WwVc/KKwDkojvPUV46nFq6puXn0syQxlyTPhe482w2Dp4+IM6i4G5zFTXNmAv3qR5xzj3nvj1jbwjotsRGRbxEu1fo2sJxQjXSNqiZEZAyhSmmUqn4ZLT8JuERVx0fj86J19gS2B04TkU+BPxIeXZ4APgEOUtXlzWx/MpBP+BwfBiwBrlTVySnL7ApcA0yIYrwNuElVvYjsATwPnAJcDgwB+jbaxrbAf4CBqlorIqcCdwJ7q+oLIjIM+AoYoaqLRGQ0cBMwkfDvPhU4X1XLo/IGETLT7wJFwIvAT1V1UTP7VwI8QHjPjlHVDt2J0Rhj0lz5MDzzdhi+4iEYNiD8BZi/FK56GG48Bd6eDb+c0nT9D7+Ai+4j+eJ8KmbVkmzmdJJPkj6spoI+JHHUxeJrZjpHXZ7Dx8KL6Nb+BbV1rKgD8sP4ysICVi5N8k1enKV5Yf1llZ6Cojpq+hVCfvM1M0kPNVGS9frXcMWrSW7ZK97ssr1ZtpufIo2D2GNdCuuUujoR6Q88RzgxDwcOAk4FzutgUT+K1ukLPAH8Cfg3sB4wLJrX2nWBRwP/ipY/A/iziOwcxTgBeAa4npC0HAScDZyYsn6ccMfDb0fba+wdoArYKRrfF5hF6LVN9PfDKKkpAl4APgLGEpKpkcAtUTwOeJyQ8GwJbAiUA/c33qiIDAdeIiRNh2Y6qSkvL7dhG7bhXB+uqCLNivSvlZr68YpqWlK7bGU0v+WTY150OxIX89TFY+DXVOnk14XhmqKCUPsC5CUSlFRVNSzno7ITjTbhfKubbaIiqn/vNse/A8OZ1E362Pi2F2k/533r5UV9ab4DNP50lwK/UdWrROR44FpgtKr6aL0zgPNUddOO1Nio6hWNtj0LuEpV57UR52RgI1XdNWXaFKBSVU8XkVuBElU9NWX++cABqrpPVGPzIrChqs5vZTsPEWqOLgMWEh7O9QtV3V5E7gaWq+p5InIUcK2qbpSy7nbA/4ASQvJUX/tTHc0fBCytP07RMXkAOBb4s6quU7tjB3Tqh8wY0w3NWQj7Xg5zFsFRO8H958Lxv4dHXoVxw+C5y2Dc8JBgnPwHuO+l9PX7lcCHv8e/OZ+qYydTUVOaMjOcID2whIHUUsAqV8Cy0tKGBAYg6aC8fwFVxQWs7lvcsE5Z/36s6lNEdV4+dbEYlev3ZdkqzzslRZTHYxQUOpYWFOHjDvrkNdT2tGRIMfzv+DjjB3aL2om1kbHALzvgzbTv+8uf3b7LD5JzrpJQ2VC/7ccJLS8NsXjvX2hvee1tirq6hc7D9UYBn9cnNZHZ0fSOmNdo/BTCbZVfEZFaYApwuarWNV6xhfXnAdtGw2OBvUQktd0uBnyRMp5sNN6c54FJwKPACsLTR/8cJSV7A2embG+0iKxotL4n1GqNBQqBRSKSOr8KGA3UPxfjFEKyc1sbcRljTPuNGw6zboPKaugT9Xt5+IJQk1NSuCYBcQ7u/Rn8+YxwmqmphdokDAl3C3YjB1NUsTWF1WF69fS5VP70KUh48s/YkaLXyigujrPg0Fp4sgaer11TtofCqgSDd+rHZ7NDlUpBnzjn3LM1/UcUUVmRpKgoRjzPsboqQVWtIy8f+hbF+KoswSXT6vhyZYKf7JDHvuPj1FfixJ1ndR30KXBU1nr6Fzpc92hyMc1bTOiKUm9Zo3EPjGtvYZ3Vx+YLYEMRcSnJzTjWJAn1dWp9UtYZ0Uw5adftqepcQpMWIrIVoVlqLuk7nGpMM+P1CcLnhBqhs1rZD98oOWvO84QmsiOA56I+RC8DPyYkLPU/az4HZqrqFs0VIiKfAxXAeqra2vWKFwH7Ac+JyIHN9S8yxpi14tyapKZe4/HG00uaznd5cVzU/6X4e1tQ/L01X3v1D0v4YOpUOCYOL9bhkyEBiQFFVQmGDytm619uwzezK9hw58H0HR620ad0TZ+Y4qI4xSmbHtE/zl1HtNRnxlGUH4YK4pbQtMZnrjKo/TF4P6Yzy+us6+GeJtQ+XCwiBSKyKXAhoWMtqrqMcKI/VUTiUZLyo7YKFZGTRaQ+AVpBeHZEopVVdhSR46Jt7AUcCdwTzbsNOFZEDhGRfBHJE5EJIrJ7R3ZUVecQErZzCf2KAKYBFwCvqWpFNO0poEBELhaRviLiRGQDETm8vijgXeAPUW0PIjJERI5ttMk6wgPBPgCmi8jQjsRrjDHdhStx7PC33ZucSvNK8hix9UC2PGJkQ1JjukY36WPTqTolsVHVMsKVPfsAiwgdeO8lXBFU72TgYKAsmn5nO4reC3hLRCqAVwkda+9rZfmHCJ1/l0fln6Wq/41i/CDa/rnA14Sqr8mEjsQd9Tyhn8yLKeP9or9E26uM4p9A6JNTRkiAtonm11+95aJ9LAdeo5ne4KqaVNUfReu/HF1tZYwxPc6w/ZpW1ucPLMhCJAZyM7Fps/NwTxF1Hq5T1dOyHUsOyI0PhTGm25g6dSoAhxxyCFOHPUCyak0L/OgTx/HtW3dqaVWTwc7Dlxz0dtr3/VVPb9vjs5tue4M+Y4wxuSl/UCHLVtaSjMeI1yVZNadrLm02TeVKLU0qu+e0McaYLhUf05dkPJx+Enkxqtu4XNtkjnfpr1yQMzU2qjop2zEYY4xp24iDRrLwwzV3whh15JjsBdPLJXOwxiZnEhtjjDE9w+ZnbErZ3FUs/O9iRuw5nE1OHp/tkEwOscTGGGNMl4rlxdj5hu2zHYYhN/vYWGJjjDHG9FKW2BhjjDEmZ+RiHxu7KsoYY4wxOcNqbIwxxmRM7Ypq5pw6nX7zllF5Ur9sh2MayZVLvFNZYmOMMSZjZox/gMSyagqB/PeXkPhRLfE++dkOy0S6w0MwO5s1RRljjMmIupU1JJZVN4zH6qBuWVUWIzKNJZ1Le+UCS2yMMcZkRKwkvVHAA/Eh9vRuk1mW2BhjjMmMZPrzdB1QNXNF88uarMjFp3tbYmOMMSYjYgXxJtOq5qzMQiSmJbmY2FjnYWOMMV2mrqwu2yGYFMncyGXSWI1NC0Rksoj8dS3XnSciJ3R2TMYY05MkaxNNpi17cGYWIjG9SY9LbETkHBGZ3WjaT0XEi8gBKdOKRaRKRA7t+iiNMcaUvbCgybTVn5StXWEfzoeDroKT/wDLV61jZKaeNUV1D9OAW0RkQ1X9PJq2N/AhsBfwbDRtFyAOTO9I4SLiovWMMcYAC8qS7HDbahZXwF4bOf51SkmTZSovf46qq16AOg8xR59HT2D59OVNlvNRLY73not+MZ9Fi+qI+zpueen3lC5eChMnwPOXQWzN7+5X/jWPW+77gollw9hk9lfssu2F9Jv7p8ztcC+SzMH72PS4xEZVPxSRrwnJzF0iEgd2B04DfpWy6N7Am6q6UkQ2BP5ASHZWA48Cv1TV1QAi4oFzgROBLYA9G29XRC4BTgIOUtXPRGQMcD0wESgmJFaHquqyRuuVAFOAnYESYBZwoao+F80fA/wF+A7hasi5wHGq+qmI7BNtYyOgBpihqvus5aEzxpi1suedq/kqqiT59yzPhf+s5tr9CxvmV/7uRap+M23NCklPxRH3sbRgXJOyEkvDfWzuu2cpixaF/jYJl8c/1t+RExc8AS++D799BC45GoBlqz3Hv1TAi8/ez0bfLAJgYWk/ihKegnjunZS7Wq7U0qTqcU1RkRcIiQvAdsBC4ElgIxEZFE3fG3heRPKAp6NlNgR2JCQ4NzQq84fAMUAp8E79RBHJF5G7gAOBnaOkpiSKYTGwGTAYOJ+QfDQWAx4DNgYGAQ8Aj4rIkGj+b4H5wLConElA/c+cewkJWX9gA+Cqdh2ddVReXm7DNmzDNtwwPH9F+mXbT32SSFum7oW03gGBB1dd23R6VBPz2cyKtMkzh6QkQe/Oayj/s+WQl0w2JDUA+YkEybLKTt3H7jxsOsZ579teqpsRkUnANaq6voj8EhipqmeJyLPAncBzwDJC01Qd8CKwnqpWROvvBzwOlKiqj2psTlbVe1O2MRnoAwwEVgAnqGpVNO9o4BZglKo26eIvIvOAS1R1SgvxLwVOUtVnou0MAv5PVT9uppwpwK2qurDDB2rt9bwPhTEmY/a5azXTZicbxq/5bh4X7b6mxqb63reoOPnh9JUK85jTfyPqFqf/3ssbV4rM/gHvvL2KW25ak6wc9c7THPzR9DDy/s2w5YYArKrxDL5pNS/ddhnf+SIkULfutC9n/+/HnbiH3V7GqlVOP25m2vf9HQ9s0uOrcHpcU1RkGjBcRCYQkpfbo+kvRuO1QBXwKnAEsKQ+qYnMBoqAIYRaF4B5zWxnV0Jtybb1SU1kDDCnuaSmMREpJjQnHUiokUkCfaNtA1wA/BqYKiJ9gEcIzWSrgMOAi4H3RWQJcIeq/r6tbRpjTGd69uQiDv9bFW9+meTordKTGoDCk7bDDSyh8sJnSX6+nNi4gfT992kMuukDFt3wfnphUQ/Gb29byvEn1vKPR5bTpzTGzieNh4+A8w9tSGoASgscM35UwA51l3D4B2+yrKQv6x24ZYb3uPfIlccopOqRiY2qfiEiM4GDgJ0ITUgQmof+Rqil+Y+q1orIF8AQESlR1cpouXGExGdJSrFJmnoKeBd4UUT2U9V3o+nzgLEiElfVptczpjsP2I3QNDYvqiFaSpSBq+oS4BzgHBEZBzwB/B9wabS9Y6IOzROBf4vIe6r6QpsHyRhjOkl+3PHUScWtLlNwyOYUHLJ52rTBx41vktgUDF3T8fi7+w3ku/sNjMbGtFj2ZoPy+Oznffn9W3uyYz84/Vs9tRdF95OLfWx6ZGITmUZIGj5T1W+iae8AQ4HvE2pJAN4gdNi9UUTOBwYAVwJ3q2qbTS6q+kcRWQVME5FDVPVVQp+d64CbReTXQAUgwIeq2rhhtB9QTWgaKxCRC6MYABCRY6IY5wFlhH46CREpAI4DnlbVpSKynJB8tZVIGWNMt1A0tl+TaSN/ve1alTWsT4xrdlvXiExv0JPT3ueB4YRaGgCi2pP/RNOfj6bVAQcDIwmddN8AXgd+0d4NqerdwI+Bp0Vk76hZay9gFPAZsJSQSOU3s/pNhD46XxGawCpJb/b6NvASsIpwZdXbrEnKjgE+iRKrJ4HLVPWl9sZtjDHZlD+w6QMv46UFWYjEtCTp0l+5oEd2HjYZZx8KY8w6S1TV8WbxnQ3jHthm/nEUj2pak2NalbGUY9IJs9O+7ydP2ajHpzc9ucbGGGNMN+Zi6edIB9QuqGx+YZMVSefSXrnAEhtjjDEZ0dzTvRPLm7vdlzGdpyd3HjbGGNODeKDvzsOyHYZJkSu1NKmsxsYYY0zGFI7r2zCcHODI61/YytKmq+Vi52GrsTHGGJMx33rv+yz43Qw++/RTVh/Zt+0VTJfKxYdgWo2NMcaYjIn3yWf0ldtTeWJ/fImdckzmWY2NMcYY00vZnYeNMcYYkzNypV9NKqsXNMYY06W+WOn5ZJndB7Q7yMX72FiNjTHGmC5z+4wEP34+JDVbD4EZJ9tpyHQuq7ExxhjTZX76/JqamneXwK1v12UxGpPEpb1ygSU2xhhjusTHlQNonMbc9k5WQjGRhEt/5QKrAzTGGNMlrlvwrSbTqhNZCMQ0yJV+NamsxsYYY0yXWJYsbjItz85CppNZjY0xxpgu0rR2oNjOQlmVi5d720fKGGNM1lhTVHblSofhVL2iElBELhaRqetYxmQR+WtnxWSMMb1P03vXLKvKQhimQcK5tFcu6BY1NiIiwCXALkAhsBB4BrhWVb9e1/JV9beNtjcdeF5Vr1rXsjNZpjHGdNgXS2H5KthqQ6ishhlzoS6J32AQlFfDlhvg8tO/+n3SU/v+YmJDSsgbkckHVTY9cVqNjelsWU9sRGRfYCpwC3CWqi4QkfWB04DdgQezGZ8xxvQYD7wMJ/0B6hJw5I7w1lyYt5SQUDiSlMBumxF77nxcQfj690nPkiMfo/Lxz6AgztCHDqPksE06PbSHloylucSm1hKbrLI+NplxG3C/ql5YPyGqpbmyflxEjgV+CYwFKoAngfNUtSKaPw+4C/gusA3wCfBjVX0zmv8bYKKq7iMitwK7AjuJyEXAAlXdVET2Bn4LbALUAdOAc1R18bruoIiUANcARwDFwCtR2fNT9u8yYCRQCfxTVU8WEQdcBZwC9AWWATeq6h/XNSZjTA664YmQ1AA8+lo0MXzNOzyOWvx/ZsKrs2H3TQGo/XRZSGoAahKU3fxmRhKbF8pHNjt9tSU2WZWwPjadS0Q2AcYD97exaBlwPDCAkJTsSmi6SnUm8DNgPeAR4BkR6de4IFU9G3gZuFJVS1V102hWNXA2MATYChhBqEXqDDcDO0avDYGlwFQRiUdJz32E2qq+wDigvi/PvsDJwHeieTsQkqKMKi8vt2EbtuGeODx6cMM0SgppKgZ5cSr65TdMWV2cxKVcmpQ3ul9GYls/f1Uz8UBRvBsct24+nEm5eIM+5332HkQmIrsQTtQTVPXjDqx3NnCSqu4Qjc8D7lPVX0fjDvgcuEhV70+tsYnmT6eN/jAicjBwl6oOjcYnA3WqeloLyzdbpojECLUwh6jqc9G0UuAbQlPbu4RE5xfAg6r6Tcq6exCStBOA6araVd3s7Ol0xvRES8rgvMmwdCVcfCS8NRtu+ze+NoEfMgjfvz+xM/bAHSVpq61+bi5lN7xB3galDLxxb+IDizo9tJsfeonz5u9M4+aojQfAzNO6Q+NBt5axlGPXM79O+75/+fb1e3x6k+1P05Lo7wZAi4lN1A/nUmAzQufiONC4iWhe/YCqehGZT2jaaRcR2Y7QFLU1UEL4IJW2d/1WDCHEPDclvlUishgYpaqvisiBwHnA1SIyh9DcdL+qTheRiwm1Uw+JyGvAxaqqnRCXMSbXDOkP9/1szfiuE+DcQ6IeNi0r3ncsxfuOzWho44tXNju9LpnRzZo22J2HO5mqzgRmAce1tIyIFACPEzoRj1bVfsCFNP0/HZOyjgNGA1+2UGxz/0oPAm8Dm0TbaDGmDlpCaOZKja8UGAp8AaCq01X1UGAwoU/NFBHZKJp3h6pOBIYDM4DHOikuY4zJusrabEfQu9nl3pnxE0J/k0XArar6lYgMA04l1HI8RajxWK6qq0VkAqEvTGOnisg/gPeBnxNqXZ5uYZsLCX17UvUj9OUpF5HRwEVrsS95IpJWh6uqVSJyL3CliHwErABuJHRwfiPa14mEZqwyEVkRrZoQkR0I+/4GITkqB6yrnTEmd+TGubTHysVnq2f9Bn1Rv5OJwATgfREpJ/S7GUroV7IK+DFwnYisAv5E852N7wD+ACwHjgEOUtWyFjZ7M+H2OStE5MNo2umES8zLCbUiD6/F7lwGrE59ichwQqKlwJvAfGB94FBVTRDeg7OAedG+/wk4WVXnEZrCbiH0wVlGuOrrmLWIyxhjuoGm3feS1hRlOllWOw93lqjz8CWqOiXbseSInv+hMMZ0K1OnTuWwT/fHN6qiGVoMi87qDo0H3VrG6rW+fdbitO/7d/40tMfXoWW9xsYYY0zvMCK/osm0ISVZCMQ0qHPpr1xgiY0xxpguceWoN5tMO3SjLARiGtTh0l65ICfq/1R1TLZjMMYY07rB+VX8YHP4W3Rzj6I4nL99PLtBmZyTE4mNMcaYnmHKQXn8ZOskc1fC98Y7+hTkRi1BT1Wbg4ffEhtjjDFdaueRMXbOdhAGgNocuXdNKktsjDHGmF4qF++PaJ2HjTHGGJMzrMbGGGNMxhWUVZEotI7C3U2lNUUZY4wxHbTRj9lvzqLwkL5H14cjdsp2RCayOvfyGmuKMsYYk0G/+hvMWQREJ5xjb8pqOCZdDS7tlQsssTHGGJM5f38lfbzWnuNrMsuaoowxxmTOiqaPUTDdSG5U0qSxxMYYY0zmVFZlOwLTmhzsPGxNUcYYYzInZldCma5liY0xxpgM8tkOwPQy1hRljDEmcypqsh2BaU0ONkX1usRGRAS4BNgFKAQWAs8A16rq1xne9hhgLjBKVb/M5LaMMaYn8d6zuBIGFUNerH0n21U1nopaj8MxtE/unaC7RA4etl6V2IjIvsBU4BbgLFVdICLrA6cBuwMPZjM+ABFxQFxV67IdizHGtKUu6Tnj30mmflTH0MoaznZLGPq/OeQlgwI8AAAW90lEQVQVxdnlqm8zqNHyHjjiH3XcfUCcAUXhrFpRk2SD25OU1UBxHnw4KcbYAek9JZLec+4LSf45z7PHKMfm68F509c0c+0+EqYdHSfeQlJU+cRMVlw8ndjAIgbddRD5mzSOrLfKvczGed972j9F5DPgZVU9tYX5JcA1wBFAMfAKcI6qzo/mTweeV9WrUtbxwK6q+oqI/AbYFXidkCwB/FlVL4uWLQP6AZWE/+9rVfXKqIxzgROBLYC9gJeAkaq6OFrXAXOAS1X1vs45Ii3qPR8KY8w6+fOMJD95PtkwPumFd5g48wsAxg6vZq//Ppy2vAdi1/2dcyXGzXuGjsX7PFTHtPlrltlhOLx+Qvrv7ikfJTnxmTXbcTT9onrh6Bh7jm7adTS5qoYvh/weXxV+LxbuNorhL53YwT3NqoxlH+4XZWmH0d/Qv8dnOr2m87CIbAKMB+5vZbGbgR2j14bAUmCqiHSkW/9uwHxgBHAocLGI7BLN2zr6u6mqlqrqlSnr/RA4BigF3gZeA05Omb8vMAB4pAOxrJXy8nIbtmEbtuF2DZdVk6YmP+XrcmUlzclLJFi6as1zpRevSr9pX0Vt020tKmv7svH8xJrtpcW5rKwhqQFIRkF3l2PY1nBGuUavHNBrEhtgSPR3QXMzRSRGSCQuUdUFqlpBqEXZHNihA9uZqaq3q2qdqr4GzACkHevdoKqzVTWhqtXAHUBqzdIPgSmquroDsayVvn372rAN27ANt2v4R99yTFgvjPdfXcNGfWPgIF4QY8zV+9KcYf3i/GrnwobxW/bJbxh2wN37uybb+tG2xciwMLzVYDhzm/QyDx4HE8eWNhtn/w2H0P/SiaH8PvkMvGbPTj8OmRzOqBxMbHpTH5sl0d8NgI+bmT+E0Jl4bv0EVV0lIouBUcCr7dxO4w7IFUB7PqHzGo0/AtwiIhOjeL8HbN/OGIwxpksMKna8f0qcZZWe/EQB/ftuTW3FFsTijrzi5k8x887MS+sgvOfoGDNPhde+hn3HwPA+TX9z9yt0vHFCnG+qYGARxJzjrG2SPPd5ksPGOcYObL1ifcDlu9HvvB1whXm4ot506mtLjmQzKXpNjY2qzgRmAce1sMgSoBoYUz9BREqBocAX0aRyoE/K/BEdDCPZ3nmqWgXcQ6ipORGYoarvdXB7xhiTcTHnGNInxoB+eTjnKCjNbzGpcTR/1dPG68U4cYtYs0lNw7rOMajYEYsuUd5icIxzt8trM6lpiLN/kSU1vUBve4d/Qugzswi4VVW/EpFhhCafucC9wJUi8hGwArgR+AR4I1r/LeAYEbkJqAKu7uD2lxASmI2B9lzufQegwM7A9R3cljHGZF+M1n/SmezKvQqb3lNjA6CqzwETgQnA+yJSTrjyaSgwHfg5IZF4k9ABeH3gUFWt79l2M6FZaDah78zTHdz+auDXwAMiskJEftXG8p8QkqkRdINL0Y0xpsOKC7IdgWlV7nWy6VWXe/dEIjIZqFHV07tws/ahMMZ0jhGnwtcr0qf5x7ITS8+Vucu9LypPv9z7d317fHbT25qiepToEvXvA9/JdizGGLNW6hJtL2NMJ+pVTVE9iYg8QmiGukZVP8h2PMYYs1ZqLbHp1nKvJcpqbLorVT0q2zEYY8w6224jmPb+mvH8jtzv1GRejmQzKazGxhhjTObccw5El3d7gD+e1uripotZjY0xxhjTARsMgoV38c5Vf6Vs/GD2OGO/bEdkcpwlNsYYYzJrSH++3GeTbEdhmuNypJomhTVFGWOMMSZnWGJjjDHGmJxhTVHGGGMy69w72ee+F/hms6Fw8ME52fzRY+XgW2E1NsYYYzLn+ifglqcp/mY1G/zvczjqumxHZNLk3mVRVmNjjDEmc65r9PiEx99ofjmTHbmRy6SxGhtjjDGZs2p1+njSHkVnMstqbIwxxmRQDlYJ5JIcfHsssTHGGJNBVkPTveVeZmOJjTHGmMyJWY+Hbi338hrrY2OMMcaY3GE1NutARKYDOwG1QAKYC1ytqg9nMy5jjOk2auqaTFq4KsluDyZZuhqu2AXO3tZORabzWI3NurtSVUuBQcBk4H4RGZ/dkIwxppuoSzaZtP7tST5bAcur4acvwKxvEq0WsaDcc8X/ktw+I0nCrqrqXLl3GxurseksqlonIv8PuBnYBpglIj8DfgxsACwH/gZcoqoJABEZAvwO2BcYAMwCjlPVT0WkBLgCOBLoD7wBnK2qs7p2z4wxpvMkALxPu/vweS94njyq+eWr6zy7PZhgTlkY/3S54+Y94xmPs9fIwbtAW41NJxGRAkISAzAz+vslcADQDzgMOBU4LVo+BjxJSGi2j/5OAsqjdf8fsBmwIzAceB14SkTyM7wrlJeX27AN27ANd8pw4/oVB01Opu8sWVNj07icBatoSGoAXv7Sd4v96sph0zHOe6vWW1tRH5vvANVAX0Jfm7NU9c4Wlr8BGK2qR4vIDsB/gcGqWtZoucHAEmBDVZ0fTYsRan0OUtVXMrRL9exDYYzpHO6ItFEPxK77e1pyc9hYePzI5hsQahMemZLgvSVh/NKdHJfv0utqbDJWreIur0r7vveXFfX4Khxrilp3V6vqVSIyELgT2DP6i4gcB5wHjCMc6wLgtWi9McDixklNZGz09z0RSZ2eD4zq7B0wxpiu4oBhJY5FKTckvmK3ls+l+XHHS8fEefATz7A+cPjG1tDQqXp8GtOUJTadRFWXi8hpwGwROQx4G5gCHAE8q6o1UY1NfaYyDxgqIv1UdWWj4j6P/m6sqku6IHxjjMmMwjhUp3cOnnt6nO89nmD2Cvj1To5vDWm9BmZAkePMbXLwDNwt5N5xtcSmE6nqNyJyE/Bb4GhCH6YlQK2I7AicCHxcvzgh+fmriJwNLAW2BJaq6lcicj9wm4icq6oLRGQAoTboOVVd1bV7Zowxa8nFiLoMNyjOd/zr+3b6MZlhdXqd7xZgfUKH4MuAJ4AVwEXAA/ULqWoSOARYDcyIlrkLKI0W+RHwKTBdRMqB94HvY/1fjDHGdJYcvNzbOg+b5tiHwhjTOQqPbnqTPv9YdmLpuTLXefjK6vTOw78u7PHpjdUFGmOMyZwef5rMcTn4/lhTlDHGmMzJt9/PpmtZYmOMMSZzthiZPl5oiY7JLEtsjDHGZM495zQMeoAbJ2UrEtMc59JfOcBSZ2OMMZmz6UiYdRsfXXEn30wYxsSzDsx2RCZVbuQyaSyxMcYYk1kbDWf2Ud/KdhSml7CmKGOMMcbkDKuxMcYYY3ora4oyxhhjTO7IvczGmqKMMcYYkzOsxsYYY4zprXKvwsZqbIwxxhiTO6zGxhhjjOmtrMbGGGOMMab7ssTGGGOMMTnDmqKMMcaY3sqaoowxxhhjui9LbIwxxhjTIufcPOfcltmOo72sKcoYY4zprVzutUVZjY0xxhjTW7lGr/au5tz2zrlXnXPvRX+3j6Zf45y7IBo+2jmXdM4Njcafcc59t9P3oXFs3vtMb8P0MM65fwKDsx1HW/Ly8gbX1dUtzXYcPZEdu7Vnx27t2bFba0u99/tna+POuXnAwd77D6LxAmAWcIr3fppzbh/gLmA8sBvwC+/9/s65vwBbAX8AHgUWAqO895WZjNeaokwT2fwH6ggRUVWVbMfRE9mxW3t27NaeHbucsSlQ472fBuC9f945VxNN/y/w9yj52QX4BXAUsAD4INNJDVhTlDHGGGM6ifd+NfAecBzwNfAisBOwNzCtK2KwxMYYY4wxHfEpUOCc2xPAObcXkB9Nh5DAXA5M895XA18Ck+iixMaaokxPdke2A+jB7NitPTt2a8+OXc/1vHOuLmX8cOAPzrk+QAVwlPe+Jpo3DbiSNYnMNGBn4I2uCNQ6DxtjjDEmZ1hTlDHGGGNyhiU2xhhjjMkZ1sfG9Hgi8lPgLKAWSKjqNlkOqUcRkT0IbeA/U9VbsxxOjyAifyJc5VENrCIcO81uVN2biGwC3AMMApYBJ6nqZ9mNyuQiq7ExPZqIHAF8H9heVbcC9stySD2KiPQFrgWezXYsPcyzwFaqujVwDfD3LMfTE9wO/ElVNwH+BPwly/GYHGWJjenpzgd+o6rlAKq6KMvx9DQ3AdcDdjfYDlDVp1S1Nhp9FRgpIvZ92gIRGQpsCzwQTXoA2FZEhmQvKpOr7B/R9HQTgB1F5H8ioiLyo2wH1FOIyAFAf1V9JNux9HBnA0+rajLbgXRjo4AFqpoAiP5+FU03plNZHxvTrYnI28DoFmYPA+KEL8eJhOdb/VdEPlXV/3RRiN1WG8duU+B3wL5dF1HP0dbnrv4ELSLHAscTno9jjOkG7D42pkcTkQ+An9QnMiJyGzBHVW/IbmTdm4hMBB4D6p/bMpjQEfYWVb0ia4H1ICJyOHADsLeqzstyON1a1BQ1ExikqgkRiRM6EG+sqkuyG53JNVZjY3q6+4H9gf+ISB9gV+Af2Q2p+1PVV4Ch9eMiMjlMtqui2kNEDib0T9rXkpq2qepiEZlBeH7QlOjvO5bUmEywxMb0dDcDd4jIh9H4var6XDYDMr3C3UAN8IhIw8Oq91bVZdkLqds7E7hHRC4FlgMnZTkek6OsKcoYY4wxOcOuijLGGGNMzrDExhhjjDE5wxIbY4wxxuQMS2yMMcYYkzMssTHGGGNMzrDExpgc4Zwb45zzzrmRGd7Omc65+1LGn3XO/V8mt2ma55yb5Zyb1M5lu+Tz0RWcc4XRvm+W7VhM92OJjel1nHPjnHMPO+cWOudWOee+cM79wzlXEM2f5Jyb1cx6LU3/QXTCuKyZedOdc9XRdsqcc+84547MzJ5lnnOuD3AF8Jv6ad77A7z312UtqDZE783EbMfRG2TiWDvn9nDO1aVO895XEx7een1nbsvkBktsTG/0DPA14XlJfYGdgH8Bbi3LOwP4Bvihcy7ezPwrvfelwCDCU43/7pzbZC23lW0nAO9772dnOxDT6z0A7OWcG5/tQEz3YomN6VWcc4MICc3t3vsyH3zpvb89+hXY0fI2JzzG4WRgfeCAlpb13tcBtxEe3LlVM2Wd5Zyb0WjaWOdcwjk3Jhq/O6phKnfOfeScO76V2H7jnHu+0bTpzrlLUsa3dM79yzm3xDk33zl3jXMuv5Vd/h6Qdmfn1DJTmjtOjuKrcM4945wb6Jz7nXNucVRTdlbK+pOiZoULnXNfR8vcmBpHW/vtnPuWc+6f0X58U7/fzrl3o0X+HdWa/bWFY1XinLsl2sZS59zjzrnRKfOnRzE9GsUw2zl3WEsHKWWffu6c+zJa5wbn3KCojJXOuU9Sazecc3nOuUudc3Occ8udc9Occ1umzM93zt2UcgwvbGa7uzrnXomOwWzn3PnOuXYn7M65I51z70a1i+865w5vvE+Nlp9cf0xbOtbOuXnRfr0STVfn3PbNlZEybZ5z7gTn3AjgWSAerbvKOXcygPd+JfAmcGh798/0DpbYmF7Fe78M+BD4q3PuJOfchI588TfjdOA97/1ThJqgM1pa0IWmrrOAWuDdZha5H9jMObdNyrRJwHTv/bxo/BVgG2AAoUlosnNuwtoE7pwbCrxEeBjmBoSaq32BX7ay2rbAR+0o/kjCE9dHA2OA14HZwAjgFOD3qYkDsGG07LgojkOAC1Lmt7jfzrn1o/14KdrWcMKTy/Hebx2t/13vfan3/rQW4r0Z2DF6bQgsBaa69Bq4k4Ebgf7ArcA9zrmSVo7BhlG846Jj8VPCSfp6YCDhuN+dsvwFhMcMHBjtw8vAc865ftH8i4CDgZ2BsdG+bli/cnQ8nonKHwIcBJwNnNhKjA2cczsDf4u2Mwi4GHjAOfed9qzfxrE+E/gZsB7wCPBMyn61VuZXhB8LiajMUu/9PSmLvE/4TBrTwBIb0xvtAUwHzgVmAIucc79ulOCMdc6tSH0RalsaOOeKCCei+pPTncABrmnnzF9F638JHAYc6b1v0lfHe78ceIJw4ieK52TgrpRl7vTeL/PeJ7z3DwLvRfuzNk4C3vXe/8V7X+O9XwBcQ+vP8BkIrGxH2Vd677+JEsmngFrv/f/z3td5758lPCvo2ynLJ4ELvPero2au6whJHdDmfp8IzPLeX+O9r4j2Ja2mqjXOuRjhOF/ivV/gva8gfDY2B3ZIWfTv3vv/ee+TwB2EBGfjVopeDVwexfMuIZl903v/mvc+QXgY5HjnXP9o+VOAa733n0S1h1cACUKCAuF9udZ7P8t7vxr4BZD6TJyfAA9775+IjtMnhASsvc9kmgQ86r1/NnqfniY8UPbUdq7fmju9929572uAawnH5uBOKHclIVkypoElNqbX8d4v9d5f7L3flvCL+v+AS4kSishc7/2A1BfhxJHq+0Ap4QQF4dfyEqBxrcDVURlDvfc7e++nthLe3cDxUTPMXlF8j0E4ATvnrnDOfRo1FawAtib8Ol8bY4FdGiVvdxFqC1qyHGjzlzahD1O9ykbj9dP6powv9t5XpozPA0ZCu/Z7DDCzHTG1ZAhQCMytn+C9XwUsBkalLPd1yvyKaDB1HxpbHCVB9Rofh/r9rS9jVKMYkoTjUB/DyGg8NYbFKeWNBY5r9H5eRmgibY+07Udmk34M1ta8+gEfHlA4n+j9XUf9CP3bjGlgiY3p1bz3ld77yYQagG3aWLyx0wn9ZT5wzi0k1MgMpOVOxO3xHFBNaIqZBDwY/ToHOI6QNB0JDIySrXdpudNzOdCn0bQRKcOfA883SuD6Rx2dW/IOsFZNX20Y2qhZZwzheELb+z2P1mtO2nrS7xLCMR9TP8E5VwoMBb5oV/Sd44tGMcSi8foYFjSa34f0pPZz4K5G72c/7/0Wa7P9yLiU7bf1eYKWj3Vq3I7Q7Fj//qaV65zLIxz7eqnJYWNbEj6TxjSwxMb0Ki50Yr3GhU6z+VGHzSMJX5Avd6CcCYR+E4cTEqL61w6EGo8D1ya+qIniXuAc4AhSmqEIv07rCCfimHPuVELNRUveArZ1zm0X7efZhF/19e4FxDl3qnOuKKoZGeec27+VMh8H9un4nrUpBlzrnCt2zo0jNLPU96Voa7+nAJu60Pm4xDlX4JxLjXEhrSQ+Uc3IvcCVzrkRUYJ1I/AJ8EYn7V97TAb+zzm3SdQf61dAHvB0NP8+4ALn3EbOuWJCc13qd/htwLHOuUNSPtsTnHO7t3P79wBHOuf2c87FnXMHED6D9U2tMwgJ6MHRZ+VwYLdGZbR0rE91zm0b1UReAJSk7NdbwN4udJQvBK4GUjuwLyR0Hk797OKc60v4f3uynftneglLbExvU0P4NfgYoQp7CXAJcI73/uEOlHMG8Lb3fqr3fmHK6z3gYVrpRNwOdwO7E5rDUk+s9xA64c4i/HqfQCvJmPd+OnAT8E9CE8gw4L8p8xcCexKudJpHaGb6B+FXekvuA7aOko/O9DnhF/xcwj7+k3Dihjb2O+pgugeh4/OXhBNhasfjXwFXuHCl0V9a2P7PASVcZTOf0HxzaJRodpXrCZcw/xtYRGiK/G509Q+E/k//Al4jHKf5hOMGgPf+A0K/lXMJ7/diQrLUrqZK7/1/CX2NbiB8Fq4DTvDevxbNn03oAHwH4X9nf+DRRsW0dKzvAP4QlXsMcJD3viya9zdCcvI2oelrPuF9ro9rJvBn4I2oia2+M/RxwIve+8/as3+m93ChudMYY9rHOXcmsIv3vl1X27SjvEmEjrt2P5Ic5JybR3h/p7S1bAfKLAQ+ICSfH3dWuSY35GU7AGNMz+K9vx24PdtxmN4rumqstX5VphezpihjjDHG5AxrijLGGGNMzrAaG2OMMcbkDEtsjDHGGJMzLLExxhhjTM6wxMYYY4wxOcMSG2OMMcbkjP8P6MPkDk3EBRwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(grouped_explanation.shap_values[0], perm_X_explain, perm_feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranked_grouped_shap_vals = get_ranked_values(grouped_explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having ranked the features by the average magnitude of their shap value, we can now see if they provide the same ranking. Yet another way to deal with the categorical variables is to fit the explainer to the unprocessed dataset and combine the preprocessor with the predictor. We show this approach yields the same results in [this](kernel_shap_adult_categorical_preproc.ipynb) example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class: 0\n",
      "The methods provided the same ranking for the feature effects.\n",
      "The ranking is: ['Marital Status', 'Education', 'Capital Gain', 'Occupation', 'Sex', 'Relationship', 'Age', 'Hours per week', 'Workclass', 'Capital Loss', 'Country', 'Race']\n",
      "\n",
      "Class: 1\n",
      "The methods provided the same ranking for the feature effects.\n",
      "The ranking is: ['Marital Status', 'Education', 'Capital Gain', 'Occupation', 'Sex', 'Relationship', 'Age', 'Hours per week', 'Workclass', 'Capital Loss', 'Country', 'Race']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def compare_ranking(ranking_1, ranking_2, methods=None):\n",
    "    for i, (combined, grouped) in enumerate(zip(ranking_1, ranking_2)):\n",
    "        print(f\"Class: {i}\")\n",
    "        c_names, g_names = combined[1], grouped[1]\n",
    "        c_mag, g_mag = combined[0], grouped[0]\n",
    "        different = []\n",
    "        for i, (c_n, g_n) in enumerate(zip(c_names, g_names)):\n",
    "            if c_n != g_n:\n",
    "                different.append((i, c_n, g_n))\n",
    "        if different:\n",
    "            method_1 = methods[0] if methods else \"Method_1\"\n",
    "            method_2 = methods[1] if methods else \"Method_2\"\n",
    "            i, c_ns, g_ns = list(zip(*different))\n",
    "            data = {\"Rank\": i, method_1: c_ns, method_2: g_ns}\n",
    "            df = pd.DataFrame(data=data)\n",
    "            print(\"Found the following rank differences:\")\n",
    "            print(df)\n",
    "        else:\n",
    "            print(\"The methods provided the same ranking for the feature effects.\")\n",
    "            print(f\"The ranking is: {c_names}\")\n",
    "        print(\"\")\n",
    "        \n",
    "compare_ranking(ranked_combined_shap_vals, ranked_grouped_shap_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in [this](kernel_shap_wine_lr.ipynb) example, for a logistic regression model, the exact shap values can be computed as shown below. Note that,  like `KernelShap`, this computation makes the assumption that the features are independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_shap = [(beta[:, None, :]*X_explain_proc_d)[i, ...] for i in range(beta.shape[0])] \n",
    "combined_exact_shap = [sum_categories(shap_values, cat_feat_start, feat_enc_dim) for shap_values in exact_shap]\n",
    "ranked_combined_exact_shap = [rank_features(vals, perm_feat_names) for vals in combined_exact_shap]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(combined_exact_shap[0], perm_X_explain, perm_feat_names )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the two summary plots above, we notice that albeit the estimation and the exact method rank the features `Marital Status`, `Education` and `Capital Gain` as the features that are most important for the classification decision, the ranking of the remainder of the features differs. In particular, while `Race` is estimated to be the sixth more important feature   using the exact shap value computation, it is deemed as the least important in the approximate computation. However, note that the exact shap value calculation takes into account the weight estimated by the logistic regression model. All the weights in the model are estimated jointly so that the model predictive distribution matches the predictive distribution of the training data. Thus, the values of the coefficients are a function of the entire dataset. On the other hand, to limit the computation time, the shap values are estimated using a small background dataset. This error is compounded by the fact that the estimation is approximate, since computing the exact values using the weighted regression has exponential computational complexity. Below, we show that the `Race` feature distribution is heavily skewed towards white individuals. Investigating correcting this imbalance would lead to more accurate estimation is left to future work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_feature_distribution(dataset, feature, category_map, feature_names):\n",
    "    \"\"\"Given a map of categorical variable indices to human-readable \n",
    "    values and an array of feature integer values, the function outputs \n",
    "    the distribution the feature in human readable format.\"\"\"\n",
    "    feat_mapping = category_map[feature_names.index(feature)]\n",
    "    distrib_raw = Counter(dataset)\n",
    "    distrib = {feat_mapping[key]: val for key, val in distrib_raw.items()}\n",
    "    \n",
    "    return distrib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_distribution = partial(get_feature_distribution, feature_names=feature_names, category_map=category_map)\n",
    "race_idx = feature_names.index(\"Race\")\n",
    "bkg_race_distrib = get_distribution(X_train[background_data, race_idx], 'Race')\n",
    "train_race_distrib = get_distribution(X_train[:, race_idx], 'Race')\n",
    "expl_race_distrib = get_distribution(X_explain[:, race_idx], 'Race')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background data race distribution:\n",
      "{'White': 89, 'Amer-Indian-Eskimo': 2, 'Black': 8, 'Asian-Pac-Islander': 1}\n",
      "Train data race distribution:\n",
      "{'White': 25634, 'Amer-Indian-Eskimo': 285, 'Black': 2868, 'Asian-Pac-Islander': 963, 'Other': 250}\n",
      "Explain race distribution:\n",
      "{'White': 105, 'Black': 20, 'Asian-Pac-Islander': 2, 'Amer-Indian-Eskimo': 1}\n"
     ]
    }
   ],
   "source": [
    "print(\"Background data race distribution:\")\n",
    "print(bkg_race_distrib)\n",
    "print(\"Train data race distribution:\")\n",
    "print(train_race_distrib)\n",
    "print(\"Explain race distribution:\")\n",
    "print(expl_race_distrib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now look to compare the approximate and the exact shap values as well as the relation between the shap computation and the logistic regression coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reorder_feats(vals_and_names, src_vals_and_names):\n",
    "    \"\"\"Given a two tuples, each containing a list of ranked feature\n",
    "    shap values and the corresponding feature names, the function \n",
    "    reorders the values in vals according to the order specified in\n",
    "    the list of names contained in src_vals_and_names.\n",
    "    \"\"\"\n",
    "    \n",
    "    _, src_names = src_vals_and_names\n",
    "    vals, names = vals_and_names\n",
    "    reordered = np.zeros_like(vals)\n",
    "    \n",
    "    for i, name in enumerate(src_names):\n",
    "        alt_idx = names.index(name)\n",
    "        reordered[i] = vals[alt_idx]\n",
    "    \n",
    "    return reordered, src_names\n",
    "\n",
    "def compare_avg_mag_shap(class_idx, comparisons, baseline, **kwargs):\n",
    "    \"\"\"\n",
    "    Given a list of tuples, baseline, containing the feature values and a list with feature names \n",
    "    for each class and, comparisons, a list of lists with tuples with the same structure , the \n",
    "    function reorders the values of the features in comparisons entries according to the order \n",
    "    of the feature names provided in the baseline entries and displays the feature values for comparison.\n",
    "    \"\"\"\n",
    "    \n",
    "    methods = kwargs.get(\"methods\", [f\"method_{i}\" for i in range(len(comparisons) + 1)])\n",
    "    \n",
    "    n_features = len(baseline[class_idx][0])\n",
    "    \n",
    "    # bar settings\n",
    "    bar_width = kwargs.get(\"bar_width\", 0.05)\n",
    "    bar_space = kwargs.get(\"bar_space\", 2)\n",
    "    \n",
    "    # x axis \n",
    "    x_low = kwargs.get(\"x_low\", 0.0)\n",
    "    x_high = kwargs.get(\"x_high\", 1.0)\n",
    "    x_step = kwargs.get(\"x_step\", 0.05)\n",
    "    x_ticks = np.round(np.arange(x_low, x_high + x_step, x_step), 3)\n",
    "\n",
    "    # y axis (these are the y coordinate of start and end of each group \n",
    "    # of bars)\n",
    "    start_y_pos = np.array(np.arange(0, n_features))*bar_space\n",
    "    end_y_pos = start_y_pos + bar_width*len(methods)\n",
    "    y_ticks = 0.5*(start_y_pos + end_y_pos)\n",
    "    \n",
    "    # figure \n",
    "    fig_x = kwargs.get(\"fig_x\", 10)\n",
    "    fig_y = kwargs.get(\"fig_y\", 7)\n",
    "    \n",
    "    # fontsizes \n",
    "    title_font = kwargs.get(\"title_fontsize\", 20)\n",
    "    legend_font = kwargs.get(\"legend_fontsize\", 20)\n",
    "    tick_labels_font = kwargs.get(\"tick_labels_fontsize\", 20)\n",
    "    axes_label_fontsize = kwargs.get(\"axes_label_fontsize\", 10)\n",
    "    \n",
    "    # labels \n",
    "    title = kwargs.get(\"title\", None)\n",
    "    ylabel = kwargs.get(\"ylabel\", None)\n",
    "    xlabel = kwargs.get(\"xlabel\", None)\n",
    "    \n",
    "    # process input data \n",
    "    methods = list(reversed(methods))\n",
    "    base_vals = baseline[class_idx][0] \n",
    "    ordering = baseline[class_idx][1]\n",
    "    comp_vals = []\n",
    "    \n",
    "    # reorder the features so that they match the order of the baseline (ordering)\n",
    "    for comparison in comparisons:\n",
    "        vals, ord_ = reorder_feats(comparison[class_idx], baseline[class_idx])\n",
    "        comp_vals.append(vals)\n",
    "        assert ord_ is ordering \n",
    "        \n",
    "    all_vals = [base_vals] + comp_vals\n",
    "    data = dict(zip(methods, all_vals))\n",
    "    df = pd.DataFrame(data=data, index=ordering)\n",
    "    \n",
    "    # plotting logic\n",
    "    fig, ax = plt.subplots(figsize=(fig_x, fig_y))\n",
    "\n",
    "    for i, col in enumerate(df.columns):\n",
    "        values = list(df[col])\n",
    "        y_pos = [y + bar_width*i for y  in start_y_pos] \n",
    "        ax.barh(y_pos, list(values), bar_width, label=col)\n",
    "    \n",
    "    # add ticks, legend and labels\n",
    "    ax.set_xticks(x_ticks)\n",
    "    ax.set_xticklabels([str(x) for x in x_ticks], rotation=45, fontsize=tick_labels_font)\n",
    "    ax.set_xlabel(xlabel, fontsize=axes_label_fontsize)\n",
    "    ax.set_yticks(y_ticks)\n",
    "    ax.set_yticklabels(ordering, fontsize=tick_labels_font)\n",
    "    ax.set_ylabel(ylabel, fontsize=axes_label_fontsize)\n",
    "    ax.invert_yaxis()  # labels read top-to-bottom\n",
    "    ax.legend(fontsize=legend_font)   \n",
    "\n",
    "    plt.grid(True)\n",
    "    plt.title(title, fontsize=title_font)\n",
    "\n",
    "    return ax, fig, df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_idx = 0 \n",
    "ax, fig, _ = compare_avg_mag_shap(class_idx, \n",
    "                               [ranked_combined_shap_vals], \n",
    "                               ranked_combined_exact_shap, \n",
    "                               methods=('approximate', 'exact'),\n",
    "                               bar_width=0.5,\n",
    "                               tick_labels_fontsize=12,\n",
    "                               legend_fontsize=12,\n",
    "                               title=\"Comparison between exact and approximate feature effects\",\n",
    "                               title_fontsize=15,\n",
    "                               xlabel=f\"Features  effects (class {0})\",\n",
    "                               ylabel=\"Feature\",\n",
    "                               axes_label_fontsize=15,\n",
    "                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_0_fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ref_4'></a>\n",
    "<a id='f_7'></a>\n",
    "\n",
    "As before, we see that features such as `Occupation`, `Workclass` or `Race` have similar effects according to the  ranking of the logistic regression coefficients and that the exact shap value estimation recovers this effect since it is computed using the underlying coefficients. Unlike in our previous example, these relationships are not recovered by the approximate estimation procedure. Therefore, whenever possible, exact shap value computation should be preferred to approximations. As shown in this example it is possible to calculate exact shap values for linear models and exact algorithms exist for tree models. The approximate procedure still gives insights into the model, but, as shown above, it can be quite sensitive when the effects of the variables are similar. The notable differences between the two explanations are the importance of the `Race` and `Country` are underestimated by a significant margin and their rank significantly differs from the exact computation. \n",
    "\n",
    "Finally, as noted in [[4]](#References) as the model bias<sup> [(7)](#Footnotes)</sup>  increases, more weight can be assigned to irrelevant features. This is perhaps expected since a linear model will suffer from bias when applied to data generated from a nonlinear process, so we don't expect the feature effects to be accurately estimated. This also affects the exact shap values, which depend on these coefficients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating the feature effects given a range of feature values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given an individual record, one could ask questions of the type _What would have been the effect of feature x had its value been y?_. To answer this question one can create hypothetical instances starting from a base record, where the hypothetical instances have a different value for a chosen feature than the original record. Below, we study the effect of the `Capital Gain` feature as a function of its value. We choose the `0th` record in the `X_explain` set, which represents an individual with no capital gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capital gain of individual 0 is 0!\n"
     ]
    }
   ],
   "source": [
    "idx = 0\n",
    "base_record = X_explain[idx, ]\n",
    "cap_gain = X_explain[idx,feature_names.index('Capital Gain')]\n",
    "print(f\"The capital gain of individual {idx} is {cap_gain}!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a dataset of records that differ from a base record only by the `Capital Gain` feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_increment = 100\n",
    "cap_range = range(0, 10100, cap_increment)\n",
    "hyp_record = np.repeat(base_record[None, :], len(cap_range), axis=0)\n",
    "hyp_record[:, feature_names.index('Capital Gain')] = cap_range\n",
    "assert (hyp_record[1, :] - hyp_record[0, ]).sum() == cap_increment\n",
    "X_hyp_proc = preprocessor.transform(hyp_record)\n",
    "X_hyp_proc_d = X_hyp_proc.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can explain the hypothetical instances in order to understand the change in the `Capital Gain` effect as a function of its value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyp_explainer = KernelShap(pred_fcn, link='logit', feature_names=perm_feat_names)\n",
    "hyp_explainer.fit(X_train_proc_d, group_names=group_names, groups=groups)\n",
    "hyp_explanation = hyp_explainer.explain(X_hyp_proc_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyp_record_perm, _ = permute_columns(hyp_record, feature_names, perm_feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot('Capital Gain', \n",
    "                     hyp_explanation.shap_values[1], \n",
    "                     hyp_record_perm, \n",
    "                     feature_names=perm_feat_names, \n",
    "                     interaction_index=None,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a logistic regression model, the predictors are linearly related to the logits. Estimating the shap values using the KernelShap clearly recovers this aspect, as shown by the plot above. The dependence of the feature effect on the feature value has important implications on the shap value estimation; since the model relies on using the background dataset to simulate the effect of _missing_ inputs in order to estimate any feature effect, it is important to select an appropriate background dataset in order to avoid biasing the estimate of the feature effect of interest. Below, we will experiment with the size of the background dataset, split from the training set of the classifier while keeping the class represensation proportions of the training set roughly the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative way to display the effect of a value as a function of the feature value is to group the similar prediction paths, which can be done by specifying the `hclust` feature ordering option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the human readable vesion of the base record (for display purposes)\n",
    "base_perm, perm = permute_columns(base_record[None, :], feature_names, perm_feat_names)\n",
    "br = []\n",
    "for i, x in enumerate(np.nditer(base_record.squeeze())):\n",
    "    if i in categorical_features:\n",
    "        br.append(category_map[i][x])\n",
    "    else:\n",
    "        br.append(x.item())\n",
    "br = [br[i] for i in perm]\n",
    "df = pd.DataFrame(data=np.array(br).reshape(1, -1), columns=perm_feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Capital Gain</th>\n",
       "      <th>Capital Loss</th>\n",
       "      <th>Hours per week</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-School</td>\n",
       "      <td>Never-Married</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Capital Gain Capital Loss Hours per week Workclass    Education  \\\n",
       "0  49            0            0             55   Private  Prof-School   \n",
       "\n",
       "  Marital Status    Occupation   Relationship   Race     Sex        Country  \n",
       "0  Never-Married  Professional  Not-in-family  White  Female  United-States  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = shap.decision_plot(hyp_explainer.expected_value[1], \n",
    "                       hyp_explanation.shap_values[1][0:-1:5], \n",
    "                       hyp_record_perm, \n",
    "                       link='logit', \n",
    "                       feature_names=perm_feat_names,\n",
    "                       feature_order='hclust', \n",
    "                       highlight=[0, 10],\n",
    "                       new_base_value = 0.0,\n",
    "                       return_objects=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  49,    4,    6,    1,    5,    1,    4,    0, 5000,    0,   55,\n",
       "          9])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyp_record[0:-1:5][10,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision plot above informs us of the path to the decision `Income < $50,0000` for the original record (depicted in blue, and, for clarity, on its own below). Aditionally, decision paths for fictitious records where only the `Capital Gain` feature was altered are displayed. For clarity, only a handful of these instances have been plotted. Note that the base value of the plot has been altered to be the classification threshold <sup>[(6)](#Footnotes) </sup> as opposed to the expected prediction probability for individuals earning more than \\$50,000.\n",
    "\n",
    "We see that the second highlighted instance (in purple) would have been predicted as making an income over \\$50, 0000 with approximately `0.6` probability, and that this change in prediction is largely dirven by the `Capital Gain` feature. We can see below that the income predictor would have predicted the income of this individual to be  more than \\$50, 000 had the `Capital Gain` been over \\$3,500.\n",
    "<a id='f_6'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction probabilities: [[0.49346669 0.50653331]]\n",
      "Minimum capital gain is: $3500\n"
     ]
    }
   ],
   "source": [
    "# the 7th record from the filtered ones would be predicted to make an income > $50k\n",
    "income_pred_probas = pred_fcn(preprocessor.transform(hyp_record[0:-1:5][7,:][None,:]))\n",
    "print(f\"Prediction probabilities: {income_pred_probas}\")\n",
    "# we can see that the minimum capital gain for the prediction to change is: $3,500\n",
    "cap_gain_min = hyp_record[0:-1:5][7,feature_names.index('Capital Gain')]\n",
    "print(f\"Minimum capital gain is: ${cap_gain_min}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.decision_plot(hyp_explainer.expected_value[1], \n",
    "                   hyp_explanation.shap_values[1][0], \n",
    "                   df, \n",
    "                   link='logit', \n",
    "                   feature_order=r.feature_idx, \n",
    "                   highlight=0\n",
    "                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that passing `return_objects=True` and using the `r.feature_idx` as an input to the decision plot above we were able to plot the original record along with the feature values in the same feature order. Additionally, by passing `logit` to the plotting function, the scale of the axis is mapped from the margin to probability space<sup>[(5)](#Footnotes) </sup>.\n",
    "<a id='f_5'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combined, the two decision plots show that:\n",
    "\n",
    "- the largest decrease in the probability of earning more than \\$50,000 is significantly affected if the individual is has marital status `Never-Married`\n",
    "- the largest increase in the probability of earning more than \\$50,000 is determinbed by the education level\n",
    "- the probability of making an income greater than \\$50,000 increases with the capital gain; notice how this implies that features such as `Education` or `Occupation` also cotribute more to the increase in probability of earning more than \\$50,000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking if prediction paths significantly differ for extreme probability predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can employ the decision plot to check if the prediction paths for low (or high) probability examples differ significantly; conceptually, examples which exhibit prediction paths which are significantly different are potential outliers.\n",
    "\n",
    "Below, we seek to explain only those examples which are predicted to have an income above \\$ 50,000 with small probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = classifier.predict_proba(X_explain_proc)\n",
    "low_prob_idx = np.logical_and(predictions[:, 1] <= 0.1, predictions[:, 1] >= 0.03)\n",
    "X_low_prob = X_explain_proc[low_prob_idx, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_prob_explanation = hyp_explainer.explain(X_low_prob.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_low_prob_perm, _ = permute_columns(X_explain[low_prob_idx, :], feature_names, perm_feat_names)\n",
    "\n",
    "shap.decision_plot(hyp_explainer.expected_value[1],\n",
    "                   low_prob_explanation.shap_values[1], \n",
    "                   X_low_prob_perm, \n",
    "                   feature_names=perm_feat_names,\n",
    "                   feature_order='hclust')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, we see that the prediction paths for the samples with low probability of being class 1 are similar - no potential outliers are identified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating the effect of the background dataset size on shap value estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shap values estimation relies on quering the model with samples where certain inputs are toggled off in order to infer the contribution of a particular  feature. Since most models cannot accept arbitrary patterns of missing values, the background dataset is used to replace the values of the missing features, that is, as a *background model*. In more detail, the algorithm creates first creates a number of copies of this dataset, and then subsamples sets of   \n",
    "\n",
    "Since the model predicts on these perturbed samples and regresses on the predictions to infer the shap values, the quality of the background model is key for the explanation model. Here we will not be concerned with modelling the background, but instead investigate whether simply increasing the background set size can give rise to wildly different shap values. This part of the example is **long running** so the graph showing our original results can be loaded instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(X_train, y_train, split_fraction):\n",
    "    \"\"\"\n",
    "    Splits and transforms a dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    split_X, _ = split_set(X_train, y_train, split_fraction)\n",
    "    split_X_proc = preprocessor.transform(split_X)\n",
    "    split_X_proc_d = sparse2ndarray(split_X_proc)\n",
    "\n",
    "    return split_X_proc_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below cell is long running, skip and display the graph instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_fractions = [0.005, 0.01, 0.02, 0.04 ,0.08, 0.16]\n",
    "exp_data = {'data': [],\n",
    "            'explainers': [],\n",
    "            'raw_shap': [],\n",
    "            'split_fraction': [],\n",
    "            'ranked_shap_vals': [],\n",
    "           }\n",
    "fname = 'experiment.pkl'\n",
    "\n",
    "for fraction in split_fractions:\n",
    "    data = get_dataset(X_train, y_train, fraction)\n",
    "    explainer = KernelShap(pred_fcn, link='logit')\n",
    "    explainer.fit(data, group_names=group_names, groups=groups)\n",
    "    explanation = explainer.explain(X_explain_proc_d)\n",
    "    ranked_avg_shap = get_ranked_values(explanation)\n",
    "    exp_data['data'].append(data)\n",
    "    exp_data['explainers'].append(explainer)\n",
    "    exp_data['raw_shap'].append(explanation.shap_values)\n",
    "    exp_data['ranked_shap_vals'].append(ranked_avg_shap)\n",
    "    with open(fname, 'wb') as f:\n",
    "        pickle.dump(exp_data, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparisons = exp_data['ranked_shap_vals']\n",
    "methods = [f'train_fraction={fr}' for fr in split_fractions] + ['exact']\n",
    "_, fg, df = compare_avg_mag_shap(class_idx,\n",
    "                                 comparisons,\n",
    "                                 ranked_combined_exact_shap,\n",
    "                                 methods=methods,\n",
    "                                 fig_x=22,\n",
    "                                 fig_y=18,\n",
    "                                 bar_width=1,\n",
    "                                 bar_space=9.5,\n",
    "                                 xlabel=f\"Feature effects (class {0})\",\n",
    "                                 ylabel=\"Features\",\n",
    "                                 axes_label_fontsize=30,\n",
    "                                 title=\"Variation of feature effects estimates as a function of background dataset size\",\n",
    "                                 title_fontsize=30,\n",
    "                                 legend_fontsize=25,\n",
    "                                )"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![background-effect](attachment:background_effect.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that with the exception of the `Capital Gain` and `Capital Loss`, the differences betweem the shap values estimates are not significant as the fraction of the training set used as a background dataset increases from `0.005` to `0.16`. Notably, the `Capital Gain` feature would be ranked as the second most important by the all approximate models, whereas in the initial experiment which used the first `100` (`0.003`) examples from the training set the ranking of the two features was reversed. How to select an appropriate background dataset is an open ended question. In the future, we will explore whether clustering the training data can provide a more representative background model and increase the accuracy of the estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A potential limitation of expensive explanation methods such as KernelShap when used to draw insights about the global model behaviour is the fact that explaining large datasets can take a long time. Below, we explain a larger fraction of the testing set (`0.4`) in order to see if different conclusions about the feature importances would be made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 1024\n",
      "Number of class 0: 763\n",
      "Number of class 1: 261\n"
     ]
    }
   ],
   "source": [
    "fraction_explained = 0.4 \n",
    "X_explain_large, y_explain_large = split_set(X_test, \n",
    "                                 y_test, \n",
    "                                 fraction_explained, \n",
    "                                 )\n",
    "X_explain_large_proc = preprocessor.transform(X_explain_large)\n",
    "X_explain_large_proc_d = sparse2ndarray(X_explain_large_proc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_dataset(X_train, y_train, 0.08)\n",
    "explainer = KernelShap(pred_fcn, link='logit')\n",
    "explainer.fit(data, group_names=group_names, groups=groups)\n",
    "explanation_large_dataset = explainer.explain(X_explain_large_proc_d)\n",
    "ranked_avg_shap_l = get_ranked_values(explanation_large_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_idx = 0 # income below $50,000\n",
    "exact_shap_large = [(beta[:, None, :]*X_explain_large_proc_d)[i, ...] for i in range(beta.shape[0])] \n",
    "combined_exact_shap_large = [sum_categories(shap_values, cat_feat_start, feat_enc_dim) for shap_values in exact_shap_large]\n",
    "ranked_combined_exact_shap_large = [rank_features(shap_values, perm_feat_names) for shap_values in combined_exact_shap_large]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+gVsP4Zdew8dmd9zT8Dej7L+Rboon4JZ3fmQivx3nUzrhsmTinirPDaXPAdqJSLNAWq6qfhb4vNb/uyhKWrxzQTL7b9AKYKCIjBTXfUdi5Au+H1ekjOreA9kYLGM57s+RrimlHl1PRG7APe29VlU3B9Jb4rbfctwT6fP8/xdEOTYi5WhBHElsn5dDnz+i8LFzGvCKqgZfMA9f92KVoZO4Ll6bcdtkF+4GMaltksS2VeCFUNqpwHJV/bIgk+uO9W1g/smeD4JaUAb7RnGJSC/cTf/vVHV1IP0M3MPN3xZjNsXZh4pzjkNdt7UBuKfyc4AHNdBlPyDp65iInCeui99W3Hb/wn8V3u5leb1NJNnzbVLXJtzxtlxVI8uKqr6Ja3EpkOS9ZSHiugCOF5G1uArwXlwrUhv5YeCPROfq7riK97bAvrEdd94q0lW/OGpqxWUzbiUX5yanJaEN7X2LqzEHfR/6vCdGmuCeLgSFY2zA1V6b+s+zcM2Ek3An2lNxzZ9pMcoWkz9JXQgchavdbxKRRwMHSktgR+gCE5lvXSncbzna8hGjXBEtY5Qx2jpNpCnuCcpWfjjo9uLW10E+FgD+gvM6rukxXGlpgLvwHo4btOHnuHX8fmBZDsFtu1KPbuYre+fgui0+AWwU957AUaGssfafVF/uvrinY2/h+mF3wnVjgKLbINo+BoF1FMWzuPV5if98Oe6k/4aPX8vnORPXJNwFt95eiBI/WZF9fyGFt23kQUBkNKXLca1gfwH+J66PbrcSxCvJvnwIbv/7a6iM+bhte3gof3i/b4q76O8N/WUHpo3cFMTafiUVLkvLGOmRz8FjM9a6+j5KWsz1l+T+G/R/uG5uN+KO0c9F5JYo+aKVJ1rZ03x5ynN/jkyfX5qZiOunPg13wxm+8f8tbr+7VFVfVNUXccfufly3mqBIOcpq+8Rcr14LQvusv8bsiBXfl+EI3LlZcN2pfobbJhvilT3KfJLZtlv8jXRQC1wlNyyYluz5ICiNku8bW3Cth2GH+O8KETcE8hxct/Wpoa+n4p64bxWRRoGHaukiEo6RcB+ieOe4iPdxN+WpuHUYTVLXMb+sz+KuW1fiKpadYpS7LK+3iZTF+TZezCLHmxdOm0Xx7y3D7sSdV2bgWrRPxZ2bg2VLdK5u6uOH948uxD9eYqqRo4qp6l4ReRPXp3BMguxf4/oJhx2K6x9cVsIxmuOeDGwSkTRcv78hqnp/JIMEXoAKSdgaoKoLcE/hGuJGopmKuxj+ErfM9UWkbqjyciiwS93oGKURb50uT3Je3+HW089wLS9hBQepP+n0xh08E0XkpcDynYF7enGOqn4SmCZ4ot7iY8S70S82VV0C9BSRdNxTh7tww2B2ijthYb/AjXJVMMytBAZZCIm2j0Gcipiq7hCRBbgTywxcH9S5gRantrj3qM7zN0mRMqQnKHdkhJ5wBf6QwP8jx9dgXP/XsHW+jF/inujUwj1lygaeFZEjgk+jy8n3+K4uuApW2Fehz+Fj8ztcP+SL4sT4xv/bnMLnnGjHUDLCZYnsB81xD3ciDvX/luX5LiKZ/beAupfLxwJjReQY4HpgqoisDu6HJVDS/bk4IuuvEUVvRIpFRH6Ge0fnflWdFCXLscAqDYxKpap7RGQV7t3GoMgNabztWqLtE8M3hPZZ30KR6Pc2euKeTF/oezvgn8om+5ArmW0b7Rr6Da4FPiz4ZDzZ80HQdyRo/YrjE9zDtrBjKTwkMuJ++2cBrsXjN1GmaY87jw4NpU/EvYcSvC8szj5UnHNcxFBf5o+Be0TkbC06OEGy17G+uMrl5ZHrlvihwUugLI+H8j7ffoNbl2EF668E95Zhv8C9HzUxMG2vYIZinKu/w1Usow2osz1KWkI1tcUF3I16RwmMPhUhbpSqnv7jUlxT4VmB7+vibvbLcjzuvlE+L/ddz1Jx26KgwuBbCIqMEJEsVd2qqo/imux/4pOX4U6+BcNy+ua9SymbZV4KnCuFR106FdcHNNn5L8I94Wqoqu9E+dvj598UN+jBX3EXwsa4pwURkYtXcB2fSWDccH/RXAoMiNM1JRKv2E9fVDVPVZ/DPeX4SaL8IekUfUrXP1pG4MLQCeli3LsTKxPEeBw4W9wQ4kf5z8H4UHi9HYmrSMYTab4+LjDd6bhBCyJW4y54GTG2baFKiaoe8JXB8bgbnZJenIrN7xNLcH3Eo5Ux3o0KuJuHFrgWziLT+zwf4ip6BUOh+u0YdWjUUliJ64ITHtnuMmCNqkZ72lxayey/UfkuFJGXdJM9fqKVB5Lfn4sj0h2nTUkmFjeK0HO4d/Wi3XCCeycsUwqP2JOK65aSG8qbgXsQs5bYSr19ApYB5/jrZ0T4uherDAdwD6giLiP5B6ul3bbLcN23g90KT+OHG83Sng9WU8J9A9dq1EJECrpaixsR8ygCXd7EdSV8CddVuV+oa3tEb9zT7uAfuN+9CrdkZ/h/18QpW3HOcYgbRWoC7mHyZbjK07Ao80v2OpYO7A117y7pPlyc46E4rSFQ/ufbyP5a0J3MP/gIVvyKe28Z676m0PoQ1zX/l7EKFONc/W/geNwDl/D+ETlnFnedAjW0xQVAVZ8TkbuAv/mNOR/XZH0srkaYi3sp+SVx4+DPEZFRuJrxCNwGi/bEq6TOE5EJwKu4A/Ec/I2Jqm4VNyzjWBHZhjuJj8J1j4o7bGg0InIdroXhRdwToGNwB8/DPt7HIvIYcK/fiT/D/fjZsbhRQUrrLj+fl0TkTtwTtztwN2hPJjMjVV0tIvcDj4vIRFyXoTTcgdBOVa/1We/D1d5/p6o7RWQI7v2Sp1R1Me5iswOY6efTGvfU7MvCERmFe3nwBXG/97MTty7fUdXncU++AG4RkUXAtmD/4Qj/VOIa3NOw9bg+9tdR+B2B4ngFmC4it+EqVedT9OIS0QCYKyIzcevnD8B0dcOqxrMQd4J9AFjn+3VHfIKrhEwR9yvKDXAVh/B6C3vb57nHT9cYNypc8L2nAyIyHPinuOEmX8CdwI7CPb27FNf94iXcvrsGdyIejnva9HGCMpSVkcC/ReQAbmSt7bhuqL2A21Q13kX9FVz5X/HHwircMd0BSFPV0aq62e9r40Vkn88ziMRPqpOiqt+JyFRgjI/zDu5cdD5uRJ7ykMz+W0BEnsa1zr6Hu2m5FHe9ei3edMVQ0v05IVVdJyJf495zWBz8TkQiD4na4brjRj6/qqobRaQ57ny9A3cDeVrg2ck2VY30eX8Q9wOET4vIX3HdXIbgWolnhIrUEXezsDVOsUu0fWKY6svyvL/2HgaMxm2/eCIPpx4Skb/hzl0jSL7VqrTb9iHcTfXzIjIedw8wHvc0P9gqUNLzwZu4a3yz4E2rr1xFft2+DvATv3/sVP8OiKq+JSIvAw+LyAhfnjuBN9T9hkukZekFXKv2TcCJgX0oX/1vW6lqkYeHPt+nGnjX1+uIuw9ZFWOZoBjnOH/D+w/c8XyXP/ePA/5PRBYEe0GQ/HXsFWCoP7c9h+sq+Ks45Y0n4fHgWzjXAZeJyErcQ6civ6FVAefbyP66QNzQ+Om4Vo2C96iSuLeMdV/zCjBE3Dsu3+GO7/AQ6InO1XfhtsciEZmGOx4PxY3a9oaqPoa7R8oDrhL3ntLeYKW3CC3GG/zV+Q/X/3ex31B7cDc/k4EWgTzNcDdGW/zKexU4NTSfXEIjYOH6Dr4TShtIYMQLfhjZ5VzcSWUX7uR6Y2i6tria6U6/EUdS/JGaMig8msUZuKbir3AH1TrcSS41ME1dXNexb3G143eAc0PzzaHoCEuR5Yk5eorPdzLugrQLdwF6FDg00TqNMS/BNTGv8mXd6LfRAP99P1wf786h6eb6GA385564pyB5uBPN+TGW8WzcQRcp+2L8sI6+LBP9uj1AlCEqfb72uIva577MX+BahBrHW/4o+09t3P66AXfT/yQ/jNAWHHZRce/u3Ivbj7fi+p2mJlq/fvpH/Dz+HOW7U3EVkTzgU1/GWQT2fUL7amC6ZX49vod78hltmc/DvZu00y/jCly/2YNwJ8mZuKeVu3An5eeBExIsT6E44fJGO25i7fM+/XTcjeU2X86PcCfkhvGOTf9dKu4GaC3uHPSNn1evUJ6/+u22BXds3krpRhXrHSV/bV+Wz31ZPgL6J3teSxQnFC/h/htlut/izklbcTeGSwmMApVMeaLsC0nvz9HWc4xy3wv8O0q6xvjLCs0/2l9OaF7dcOen7/zfq0QZjQfXZXZcgvImc365KTRtoXUUWI4PcOe8FbhjPupIWaHprsQ9QMvDPWQ6PbzdYkxXqFwl2bah+Z2EG/4/H3fOuQh3zzA1mfNBjHnXwT0YvTLGsRX+yw3la4S7Wf3ex30UaBpl/084r0TrMZA+n9CojzGmj3uOw1Vgd1J45KzauHdJlvLD0PsJr2NEP/ZH4s5pO3EPHo+Jsm8U2Z/C86L4x0MP3H6+23+XQfTrSUnPt0XmFWO9n0jR/fUdCo+CV5x7y6j3NbgKxtN+XXzr8wwKrbO452qf5zDcvhu538zF3XMcH8jTH3es7SHBdS/y+wWmnIhIFu7G9wRVTdRlx5gSEfeDTTer6r2VXRZjfqxE5GRcZb21qn6TKH85lqM97kFPW1XNraxyVHci0gZ3MzVYVR8qg/ndjdsmvRJmrmTi3v/8FuiuUVppyimmXcdMQjX5HRdjjDGmwqjrjvMSrqtOZRoGPGKVluSIyGgRuUpEskRkAK4b7UaS7OIcxySgi7gX6Ku6G3C/01MhlRZjiqvGvuNijDHGVILhuK7BlcIPLLIOeKyyylCNKTAO17UlH9eFdYQGfpOsVDNX/UJErsG9kxTv3biqYCuxB4kwptJYVzFjjDHGGGNMlWddxYwxxhhjjDFVnlVcjDHGGGOMMVWeveNiyk2jRo20bdu2FRZv586d1KtXz+JZPItXw+PV5GWzeBbP4lVevOXLl29S1WYVEsyUTKLxue3P/kr6165dO61IixcvtngWz+L9COLV5GWzeBbP4lVePEK/qWJ/Ve/PuooZY4wxxhhjqjyruBhjjDHGGGOqPKu4GGOMMcYYY6o8q7gYY4wxxhhjqjyruBhjjDHGGGOqPKu4GGOMMcYYY6o8+x0XY4wxxlQ727ZtY8OGDezduzduvoYNG/Lxxx9XUKksXlWNl5KSQvPmzTn44IPLoFSmsljFxRhjjDHVyrZt2/j2229p1aoV6enpiEjMvNu3b6dBgwYVVjaLV/XiqSp5eXl8+eWXAFZ5qcasq5gxxhhjqpUNGzbQqlUr6tatG7fSYgyAiFC3bl1atWrFhg0bKrs4phSs4mKMMcaYamXv3r2kp6dXdjFMNZOenp6wa6Gp2qziYowxxphqx1paTLJsn6n+rOJijDHGGGOMqfKs4mKMMcYY8yO2Z88esrOzWbFiRWUXJSkZGRmMGDGi4PPAgQPp2LFjJZbIlDcbVcwYY4wxNULGqAWVEjf3jl6VEres7Nmzh/Hjx5ORkUGHDh0quzjGxGQtLsYYY4wxxpgqz1pcTLnJ27u/Qp9+DT9hHwMtnsWzeDU+Xk1etnC86v4k3yT2+uuvM2bMGJYtW0Z6ejoXX3wxd911Fw0aNKB37958+umnrFixomAUtSlTpnDbbbfxzjvvkJmZyZo1a5g0aRJvvvkmmzdvpk2bNgwaNIjf/OY31Kr1w/PpzZs38/vf/55nn32WLVu2cOSRR3LDDTcwdOjQgt9Jufrqq7n66qsBWLduHRkZGXHLvmrVKoYPH87bb79Nfn4+RxxxBDfddBNDhgwBICsri6ZNm9KrVy9uv/12Nm7cSN++fXnwwQd5//33uemmm/joo4/o2LEjDz/8MEcccUTBvEeNGsWCBQtYt24djRo14uyzz2bKlCm0aNGiLFe/qWas4mKMMcYYUwnefPNNunfvzkUXXcS8efPYvHkzo0aNYsuWLcybN4+ZM2eSmZnJ6NGjmTp1Kh9//DFjxoxh/PjxZGZmAvDVV1/Rvn17+vfvT4MGDVixYgXjxo0jLy+P0aNHA5CXl0dWVhYbNmxg3LhxHHvssaxdu5a1a9cCsGjRIrp27cqYMWPo1ctVllu2bJmw/H369OG4447jkUceITU1lXKGuyUAACAASURBVNWrV7Nt27ZCeZYsWcKmTZuYNm0a69evZ9iwYaSnp7N06VJGjhxJvXr1+M1vfsPgwYN58cUXC6bbsGEDv//972nUqBE7d+5kypQpdO3alZUrVxaqkJkfF6u4GGOMMcZUglGjRnHmmWcyZ86cgrRWrVrRrVs3Vq5cSWZmJvfeey/9+/enT58+jB49mpNPPrnQC+lZWVn06dMHcL8Q37lzZ3bt2sXMmTMLKi4PP/wwq1at4t133y14h6Vr164F8zj11FMBOProo+nUqVOxyr5p0ybWrVvH/PnzOeGEEwDo1q1bkXw7duxg/vz5NGzYEICcnBxmzpzJq6++yllnnQW4yteQIUPYtWsXdevWBeDvf/87ANu3b6du3bqcccYZtG7dmjfeeKNgOvPjY1VWY4wxxpgKtmvXLt566y0uu+wy9u3bV/DXuXNnUlJSWL58OQD9+vXjkksuoVevXqxatYp//OMf1K5du2A+u3fvZty4cbRt25bU1FRSUlK47bbbWLduHfv27QNci8rJJ59cpi/eN27cmMMPP5zrr7+eOXPmxPxF+o4dOxZUWgDatm1LnTp16Ny5c6E0cBWYiBdeeIEzzzyT1q1bc9BBB9G6dWsA1qxZU2bLYKofq7gYY4wxxlSwLVu2sH//fm688UZSUlIK/lJTU9m7dy+ff/55Qd5+/fqRn59P9+7dOeaYYwrNZ+zYsUyePJnBgwezcOFCli1bxpgxYwBXqQH3fktxun4lo1atWrz88su0aNGCa665hhYtWvDzn/+c9957r1C+Ro0aFfpcp04dGjRoUKi7V506dQqVd9myZVxwwQW0bt2aGTNm8NZbb7FkyZJCecyPk3UVM8YYY4ypYI0aNUJEyM7O5vzzzy/y/WGHHQbAtm3bGDZsGB06dODZZ5/lpZde4txzzy3I98wzz3DzzTczcuTIgrQFCwoPJtGkSZOC91nK0rHHHsuTTz7J3r17ef311/nd735Hr169+OKLL0r1HsrTTz9Ns2bNmDNnDjt27KBBgwb873//K8OSm+qqWrS4iEi2iKiIfBrj+0/999llFC/Dz693IG2kiGSVcH45IjIvQZ4UEblVRFaKyC4R2SQiS0VkVCBPO78uGsWbV4z5N/fTZiS/BMYYY4wpS/Xq1aNTp06sXr2ajh07FvmLVFyGDh3K/v37Wbx4MVdccQXXXnstW7duLZjP7t27SU1NLfi8f/9+Hn/88UKxunXrxnvvvccHH3wQtSzhFo9kpaSk0LVrV2699Va+/vprvv/++xLNJyIvL4+UlBREpCBt9uzZpZqnqRmqU4vLbqCNiHRU1XciiSJyKpDhvy8rXwNnAJ8E0kYC9wI5ZRgn6F6gPzABWAo0AjoBfYA7fJ52wDhgFpDsWaG5nzYHyC1tYY0xxhhTOhMnTqRbt27UqlWLSy+9lAYNGrB+/XoWLFjAhAkTWLNmDbNmzWLhwoU0atSIadOmkZmZyS233MKsWbMA6NKlC9OnT6dt27Y0btyY6dOnk5+fXyjOgAEDmD59Oj169CA7O5v27duzbt061qxZwx133EGdOnVo06YNTzzxBJmZmaSlpXHiiScWVGii+eCDDxgxYgSXX345Rx11FFu2bOHOO+/kpJNOonHjxqVaL+eccw5Tp05l6NChdOvWjRUrVvDII4+Uap6mZqhOFZedwLvAL4F3Aum/BBYBp5RFEBFJU9XdwJKymF8xY9YFrgZuU9VJga+ekuDjhmomnXxWp11RYfFyao0nN22cxbN4Fq+Gx6vJy1YkXnY5B8vemjiPKTedO3fmtddeY9y4cVx55ZXs37+fI488kp49e5KamsrgwYO59tpr6dmzJ+BeiJ85cya9e/fmkksuoU+fPkyaNIkRI0YwZMgQ0tPTueqqq+jbty+DBw8uiJOWlsaiRYsYNWoUY8eOZdu2bWRkZHDjjTcW5Ln//vsZMWIE3bt3Jz8/P+HvuLRo0YJDDz2UCRMm8NVXX9GoUSO6dOnCnXfeWer1cv7553PnnXcybdo0Zs6cyRlnnMHzzz9Pu3btSj1vU71Vp4oLwONAtoj8VlXV39RfBowlVHERkTOA0cCpwMHAp8AkVZ0dyDMQeAg4HZjo//2TiPwTWAf0UdXnRSQXaAKME5HI1auLquaIyHBc5akdrtXnbWCYqibTmbQekAJ8E/5CVdWXNQt4ziev8/WZ/6lqhoi0xLXUZAEtgc+BJ4DbVXWP7x72oZ92caQu5FdhZB00UNUdgXWTC8xT1RH+c2fgz8BJPst/gQmqOjeJ5TTGGGPKTbQf7Ny+fXvBDyxWRaeffnqh3y8JCo6yFdGrVy/8rQEAzZs35+mnny6Sb9CgQYU+N2nShJkzZzJz5syosXr06BGzK1k0zZs355///GfcPDk5OUXSsrOzyc7OLpSWlZVVaJkARo4cyciRIwttv3Ce3NzcQp8jrVCm5qoW77gEPAUcCkTG0Ps50Mynhx0JvAn8Gtfd6kngIRHpFyXvY7hKwfnA81G+7wtsBf6G60J2Bq71B6A1rpvXhcAgoDbwHxFpGGU+UanqRlxlI1tELhaRaGfYd4HIwO0X+zL09Z+bAt8BtwI9gUm4Fpxp/vuvcd3QAIYElqFYRORg3Hr5L3AJcCnwT1x3NmOMMcYYY8pdtWpxUdXvReRFXAvH6/7fF1V1a7hHlaoWvJnmW2Zew1UyBuEqKkH3qOrdgfwZoXm9JyL7gC9UdUnou2GB6WoDrwAbcBWZh5NYvIG4FqUngQMi8p7/fI+q7lHVbSKy2ud9T1VzA2X4kB8qNYjIm7iudX8XkZtVNV9EIo9RPgovQzG0AxoCN6nqdp/2cpLzMMYYY0w1ceDAAQ4cOFAkPfJ7MwcdVK1uIU0NUR33useBqSJyK+7J/2+iZRKRQ4DxuApEK1xLCMCXUbIviJJWLCLSCfgj8FMg+DZaUh0xVXWRiByNa/Xp7v8mAReJyFmqWvTs8UMZBLgFGAy0AdICXx8BlHYMxM+AHcCjIvIg8KqqRh0cQEQG+3LQrGlTctqPL2Xo4tuRepjFs3gW70cQryYvW4XHy8lhx44dUbv0lJeyiNewYUO2b9+eOCNulK3i5i0LNSXe9ddfz6OPPhrz+w8//JAjjzyyzOOGlfXy7d69u0L3d1O2qmPF5VngQdw7HfX44b2PsFm4Ubn+CHwEbANuwFVkwr4tSUFE5Ahcy8PbwHXAV8AeXEUoLc6kUfnWjDnAHF8ZGQ/8AdfVbX6cSYfiKjl3Aq8CW3Dv9kwvSTmilGuLiJyDe030CaCWiLwM3Kyq/w3lnQHMAGh/1OGatboCX2htPx6LZ/EsXs2PV5OXrcLj9dtKTk4OWVlZFRMPyiTexx9/XOz3Vir6HZeaEm/ChAkMGzasSPrOnTupV68e7dq1izvqWFkp6+VLS0vj5JNPLrP5mYpV7SouqrpTRJ4HhgFzVXVnOI+IpAG9gSGqen8gPdY7PRojPZGeQF3gwkg5ROQgCre8lIgffGASruJyLPErLr/AvUh/WyRBRH5SzFCRYaTDZ59DQuVZAvQUkXRca9BdwKO4yqExxhhjapCMjIyoo4pV9cEOTM1W7Sou3n1AKnB/jO9TcQMPFAxk7l94v4CSV1L2ULT1Ih04AOwLpF1GkutVRFKAelG6Xx3j/420CO3x/0YrR34orX/oc6xpv/D/HocbzAAROR03ElsRqpoHPCcimbhR22JLqQvZn8fNUqZycqBfBQ7tafEsnsWrnHg1edkqI54xxlQT1bLioqo5xPkhSP+y/jJgrIhsw1UuRuFGBot6Q14MnwC9/OAAO4DVuN+PqY0brexvwPG4l+ST/XHIhsAaEfkHsNiXsz2uYvAlEBnnMPJy/nUi8jiwy7+Y/wrwGxFZinsfpT/QNhRjPZAHXCUiW4G9/oc83/Yx7hGRP+Bai0biutYBICK9gGuAZ/x8WuG6xi1KcjmNMcYYY4wpkeo2HHIyrsAN3/swcDdutK5kRvkK+y1upK4FwDLgFF9pGIj7/Zfnfcxf4CoeydiG+x2ZU4CZwIu4ysNLQCdV3Qqgqv/DVYwuxrWORN7vuR03Utr/+X/3EBq0wP+o5iAf41W/DKjqHtywygeAecBw3LtAWwKTr8W1VP0J907PRF/Ga5JcTmOMMcYYY0qkWrS4qGo2CX4/WFWbhj6vBbpFyZodyDML9xJ/eF65gITSlhPlfQ5V/SfuN02CMkJ5smIWnILKwx3+Ly5VnQJMCaXtwP1uS1h4GWYDs8OZVHUZ7mX+oIzA96txI7gZY4wxxhhTKWpyi4sxxhhjjDGmhrCKizHGGGOMMabKs4qLMcYYY8yP2J49e8jOzmbFihVJTTdw4EA6duxYTqUypqhq8Y6LMcYYY0xC2Q2LJFXIL45kV+/hq/fs2cP48ePJyMigQ4cOlV0cY2KyFhdjjDHGGFNp8vLyKrsIppqwiosxxhhjTCV5/fXXOfvss6lbty5NmjRh0KBBbN++HYDevXvTvn37Qjf2U6ZMIS0tjZUrVwKwZs0afvnLX3L44YdTt25djj/+eKZOncqBAwcKxdm8eTPXXXcdLVu2JC0tjfbt2zN16lQAGjRw7VJXX301IoKIkJubm/SyfP3111xzzTUcddRRpKen065dO8aMGcOePXsK8uTm5iIizJ49mwEDBtCoUSP69OkDQH5+PjfccAONGjWiSZMm/Pa3v2X69OmIFBokle+++47Bgwdz6KGHkpaWxplnnsnSpUuTLq+pfqyrmDHGGGNMJXjzzTfp3r07F110EfPmzWPz5s2MGjWKLVu2MG/ePGbOnElmZiajR49m6tSpfPzxx4wZM4bx48eTmZkJwFdffUX79u3p378/DRo0YMWKFYwbN468vDxGjx4NuBaNrKwsNmzYwLhx4zj22GNZu3Yta9euBWDRokV07dqVMWPG0KtXLwBatmyZ9PJs2rSJxo0bc9ddd3HIIYewZs0asrOz2bhxIw888EChvCNGjODiiy9m7ty51K5dG4CRI0cya9Ys/vSnP3Hcccfx0EMP8dhjjxWaLj8/n+7du/P9998zadIkmjdvzn333Uf37t359NNPadGiRdLlNtWHVVyMMcYYYyrBqFGjOPPMM5kzZ05BWqtWrejWrRsrV64kMzOTe++9l/79+9OnTx9Gjx7NySefzIgRIwryZ2VlFbRYqCqdO3dm165dzJw5s6Di8vDDD7Nq1SrefffdgndYunbtWjCPU091P+V29NFH06lTkZ+sK7YTTjiByZMnF3z+2c9+Rr169bjmmmuYNm0aderUKfiuU6dOTJ8+veDz5s2bmTFjBrfffjvDhg0D4Nxzz+UnP/lJoRiPPPIIK1euZNWqVRxzzDEAdO/enfbt2zNlyhQmTZpU4vKbqs8qLsYYY4wxFWzXrl289dZbTJs2jX379hWkd+7cmZSUFJYvX05mZib9+vXjqaeeolevXtSuXZsVK1YUtFAA7N69m8mTJzN79mzWr1/P3r17C77bt28fBx10EIsWLeLkk08u9xfvVZW7776bGTNmsG7dOnbv3l3w3fr162nbtm3B50jLTsSHH37I7t27ueCCCwrSRITzzjuPTz75pCDtX//6F6eccgpt2rQptN7OPvts3nnnnfJYLFOF2DsuxhhjjDEVbMuWLezfv58bb7yRlJSUgr/U1FT27t3L559/XpC3X79+BV2kIq0MEWPHjmXy5MkMHjyYhQsXsmzZMsaMGQNQUHHYvHlzibp+JWvq1KmMGDGCvn37Mn/+fN5+++2CVpVgJQbg0EMPLfT5m2++AaBZs2aF0ps2bVro86ZNm1iyZEmhdZaSksJDDz1UaJ2ZmslaXIwxxhhjKlijRo0QEbKzszn//POLfH/YYYcBsG3bNoYNG0aHDh149tlneemllzj33HML8j3zzDPcfPPNjBw5siBtwYIFhebVpEmTgvdZytPcuXO59NJLmTBhQkHaRx99FDVv+IX7yLspGzdupHHjxgXpmzZtKpSvcePGdOzYkfvuu6/IPFNTU0tcdlM9WMXFlJu8vfvJGLUgccYyMvyEfQy0eBavmGb1rFdhsYwxJqxevXp06tSJ1atXM3bs2Jj5hg4dyv79+1m8eDFDhgzh2muvZeXKlTRs6H6zZvfu3YVu2Pfv38/jjz9eaB7dunVj7ty5fPDBB5x44olFYkTePQm3iiQrLy+vSOVh9uzZxZr2hBNOIC0tjfnz5xdUwlSVF154oVC+bt268fLLL3PEEUfQvHnzUpXXVD9WcTHGGGOMqQQTJ06kW7du1KpVi0svvZQGDRqwfv16FixYwIQJE1izZg2zZs1i4cKFNGrUiGnTppGZmcktt9zCrFmzAOjSpQvTp0+nbdu2NG7cmOnTp5Ofn18ozoABA5g+fTo9evQgOzub9u3bs27dOtasWcMdd9xBnTp1aNOmDU888QSZmZmkpaVx4oknFnqZvjjOOecc7rnnHk4//XSOPvpoZs+eXeyWnshQ0OPGjSMlJaVgVLHt27cXap0ZMGAA999/P1lZWYwYMYKjjjqKzZs38/bbb9OiRYuCF/tNzWQVF2OMMcbUDFF+wX779u0Fv1NS1XTu3JnXXnuNcePGceWVV7J//36OPPJIevbsSWpqKoMHD+baa6+lZ8+egOsmNXPmTHr37s0ll1xCnz59mDRpEiNGjGDIkCGkp6dz1VVX0bdvXwYPHlwQJy0tjUWLFjFq1CjGjh3Ltm3byMjI4MYbbyzIc//99zNixAi6d+9Ofn4+69atIyMjI6nlGTt2LBs3bix4x+biiy/mnnvuKRj1LJGJEyeyd+9esrOzqVWrFldeeSVXXnlloW5haWlpLF68mLFjxzJu3Di+/fZbmjdvzmmnnVboxX5TM1nFxRhjjDGmkpx++um8+OKLUb/76quviqT16tULVS343Lx5c55++uki+QYNGlToc5MmTZg5cyYzZ86MGqtHjx588MEHyRS9oNUnon79+jz00ENF8gXLm5GRUehzUFpaGvfdd1+hikqXLl046aSTCuVr2LAhd999N3fffXdS5TXVn1VcjDHGGGNMpVu8eDFLly7lpz/9KXv37mXOnDnk5OQwd+7cyi6aqSKs4mKMMcYYYwo5cOAABw4cKJK+b9++gt+HKWv169fnmWee4c9//jO7d+/mmGOO4b777uPSSy8t81imerLfcYlDRLJFRGP8/SrOdPNEJKcCixqM3UNEhkZJnyUi9stMxhhjjEnommuuKfJbKSkpKTRu3JiUlBRyc3PLPOapp57KkiVL2Lp1K/n5+axcuZL+/fuXeRxTfVmLS2JbgZ5R0st/QPSS6QFcCkwNpf8RSK/44hhjjDGmusnOzuamm24qkr5z507q1atX8DszxlQkq7gktk9Vl1R2IUpLVT+r6Jjp5LM67YoKi5dTazy5aeMsnsUrXryvx0P2hcWfIMpoRcYYU1NlZGREHVWsKo/SZmo+6ypWSiJyuIgsFJE8EckVkWuj5CnSTUtEMnyXs96BtNoiMlpE1ohIvoh8ISKzAt/3EpFXRGSDiGwTkSUi0iPwfTYwHDgy0KVtVpwydBCRf4vILhHZIiKzReTQKGW8TEQeEJGtvkzjRcT2HWOMMZUm1shUxsRi+0z1Zy0uxSAiRdaTqu4T94tI84GmwK+B3cB4oDHwaQlCPQAMACYCr/r5XBL4vg3wHDAZOACcB7wgImep6pvAg8AxQFegr59mY4xlagbkAB8DVwD1gTuAV0Sko6ruCWSfCDyJ64LWDRgLrAKeKMEyGmOMMaWSkpJCXl4edevWreyimGokLy+PlJSUyi6GKQWruCTWBNgbThSRNsBPgJOBTqq61KcvBz4jyYqLiByLq/zcoqr3BL6aE/mPqt4byF8LWAwc76d7U1W/EJGvgfxidG8b7v89V1W3+Xl+CizBVZYeC+R9TVUj+V8RkZ7AxVjFxRhjTCVo3rw5X375Ja1atSI9Pb3QL6sbE6aq5OXl8eWXX3LooYcmnsBUWWLNZrH5rldDge5Rvv4A+D1wvaq2CE23FMhT1Sz/eRaQqaodA3kygHVAH1V9XkRuAP4KNIxUJKKUpzUwwZenJRA5U7+pqp19nsnApaqaEZq2UBlEZBGwSVUvC+VbByxU1SGBMl6pqo8E8jwKHBGJGZp+MDAYoFnTpqc8cc9t0RalXOxIPYz6+UV/rMviWbwyideyQ+ni7dhB/fr1SzUPi1fxsSxe1Y1Xu3Zt6tWrl7DSoqoVWrGxeFUznqqyc+dO9u/fHzNPly5dlgfv1UzVYy0uie1T1ajDCItIC2BDlK82AMm+udYE2Bmn0lILeNbPdyxuVLOdwO1A8yRjgav4rIqS/i2ui1rQ96HPe4C0aDNV1RnADID2Rx2uWasr8GXr9uOxeBav3OL1K93L+Tk5OWRlZZVqHhav4mNZPItn8X5c8UzVZhWX0vmG6JWG5kBe4PNuoE4ozyGhz5uBeiJycIzKS1tct7TzVPXFSKKIlHSI46+JXvZDgeUlnKcxxhhjjDHlwioupbMMGCcipwfecTkC+CnwZiDfF0CGiKSp6m6f1qPwrFjk/x0A3EtRkQpKfiRBRI4EfobrthYRszUkZClwg4g0UNXtfn6nAhnAG8WYPrGUupD9eZnMqlhyckr9VNziWTxjjDHGVE1WcUnsIBHpFCX9c2Ah8D4wV0R+h6tUjKdo97FncF26HvTvmpwMXBPMoKqrRWQGMEVEmgOvAY1w76v8EvgEVwGaIiJ/wHUZGw98GYr1CXCoiAwEVuLeY8mNUv67gBuAl0TkTn4YVexD3AhixhhjjDHGVBn2WxyJNQTeivJ3tbqRDS4APgL+DvwF11ryVnAGqroSV1E5A/eeytnA1VFi3YirjPwKVymaCuzy88jHjeS1D5gH/BH4M27Y5KAngFm4IYyXAdnRFkpVNwJdcN3YHgOmA68D54SGQjbGGGOMMabSWYtLHKqaTYwb/0Ce9UDPUPIDUfLNwlUogiSUZz/wJ/8XLdYy4LRQ8qxQnt1EqRSp6sAoae/hfvMlKt9SU2Qoj2jzMsYYY4wxpjxZi4sxxhhjjDGmyrOKizHGGGOMMabKs4qLMcYYY4wxpsqziosxxhhjjDGmyrOKizHGGGOMMabKs4qLMcYYY4wxpsqziosxxhhjjDGmyrOKizHGGGOMMabKs4qLMcYYY4wxpsqziosxxhhjjDGmyrOKizHGGGOMMabKs4qLMcYYY4wxpso7qLILYGquvL37yRi1oMLiDT9hHwMtnsWLI/eOXuU6f2OMMcaUH2txMcYYY4wxxlR5VnExxhhjjDHGVHlWcTHGGGOMMcZUeVZxMcYYY4wxxlR5NbriIiKXiMgiEfleRPJFZI2I3CUih5VDrCwRURHJ9J/riEi2iHQo4fxyRWRyMfKlisgwEVkmItv9cn4qIveLyPEliKsiclNJymyMMcYYY0x5qbGjionIFGAo8BDwF2Ab8BPgeqAN0LeMQ74LnAF85j/XAcYBucCKMo4FgIjUBV4GTgCmAWOAPUAmMAjoBRye5GzPANaVYTGNMcYYY4wptRpZcRGRPsCtwK9V9e+Br14VkRlAj7KOqarbgCVlPd8EJgAdgNNVdVUgfbGITAd+newMVbXMliGdfFanXVFWs0sop9Z4ctPGWTyLV1T21vKZrzHGGGMqTE3tKjYMeDdUaQFAVfer6guRzyJyh4h8KCI7ROQLEZktIi2C00S6bYnIH0TkG593tog0DOQp1FUM2O7/fcinq4hkFDdmIr61ZTDw11ClJbKcB1R1ZiB/PRG5V0RWi8guEVknItNF5ODQfAt1FRORHBGZJyJXiMhaEdkmIi+ISOtkymuMMcYYY0xp1LgWFxFJAc4EphRzkubAn4CvgGbAcGCRiGSq6oFAvn7AWlwXrJbAROBB4Bcx5tsVWAT8HxD5Vb2vk4wZzylApKtYcdQFagO3ARtxXchuA+YC5yaY9nTgMF/OdOBuYAZwfjFjG2OMMcYYUyo1ruICNAFSgfXFyayq10T+LyK1gbeAL4DOwGuBrOlAL1Xd4fPuBP4pIsep6sdRZr3M//tZuPtVEjHjiQww8HkwUURqEWhJU9V9/t+NwA2BfAfh3mV5Q0SOUNV46+tg3LJv8dO2AP4iIumqmlfM8hpjjDHGGFNioqqVXYYy5W+qvwauU9UZxch/HvAH4HjcDXrEIFV90OfJBZaq6uWB6eoCO4GrVPVhEckCFgMnqOpKEamP6y52tarOKmHMeao6Ika5LwceB9qr6ppA+vO4l/IjTlDVlf67K3Hv/hwD1AvkOUdV/+XzKHCzqt7rP+cA+1S1eyBGD+Al4BhVXRsq12BcFzaaNW16yhP33Bat+OViR+ph1M//yuJZvKJaFh3cb8eOHdSvX7984kVh8apnLItn8Szejydely5dlqtqxwoJZkqkJra4bAbygSMSZRSRU4FngaeBO4ANgOJesk8LZd8Q/KCqu0RkB67bWLElGTOeyB1ea2BNIH0okI3rSnZ/IG5f4GHgPuD3wHe+7E8XI+73oc97/L9FpvOVxRkA7Y86XLNWV+DL3e3HY/EsXlT9ir6cn5OTQ1ZWVvnEi8LiVc9YFs/iWbwfVzxTtdW4iouq7hWRN3HvbYxJkL0v7n2Py9U3PYnIkTHyNg9+8C0u9fnhvZXiSiZmPMuBXbgR0hZFEiMtIL7FJ+gXuFajGyMJInJ2CeIaY4wxxhhT4WrqqGJTgY4iclX4CxGpJSI9/cd0YK8W7i/XP8Y8zwlVBvriWkreiZE/VqtEMjFjUtVduJaNISJyXDEmSce1RAUlHdcYY4wxxpjKUONaXABU9TkRuQv4m4j8DJgP7ACOxf0AZS7wIvAKMFREpgLP4UYj+1WM2eYBC0RkEq6L1STgaVX9KEYZ9ojIOuAyEVkJ7AY+SDJmIrcBpwFvici9wOs+TivgKmC/Lzc+7nQRuQ1YihsRrFsJ4xZPkwe3RwAAIABJREFUSl3I/jxxvrKSkxO1S5DFs3jGGGOMqf5qZMUFQFWHi8h/gJuAR3EtDrm490sm+zwLReR3wM24YY7fAnpT+J2RiMdxL9v/DddF7FkCo3TFcL2P9S/cSGdtkoyZaBl3iUhXYAhwBe79loNwI439GzhJVT/z2R8AjgJuwbUCveKnqegfzTTGGGOMMSZpNbbiAqCqTwJPJsgzEfebLEESPatm4158j/ZlTng6VX0ZOLEkMVU1I3apC+XLB+7yf/Hy7QdG+L94ccOfs6LMKyc8nTHGGGOMMeWppr7jYowxxhhjjKlBrOJijDHGGGOMqfJqdFexslLcblvGGGOMMcaY8mEtLsYYY4wxxpgqzyouxhhjjDHGmCrPKi7GGGOMMcaYKs8qLsYYY4wxxpgqzyouxhhjjDHGmCrPKi7GGGOMMcaYKs8qLsYYY4wxxpgqzyouxhhjjDHGmCrPKi7GGGOMMcaYKu+gyi6Aqbny9u4nY9SCCos3/IR9DLR4Fi8g945eZTo/Y4wxxlQea3ExxhhjjDHGVHlWcTHGGGOMMcZUeVZxMcYYY4wxxlR5VnExxhhjjDHGVHlWcanBRGSgiCwXke0iskVE3hORuyq7XMYYY4wxxiTLKi41lIiMBh4EXgIuBgYA84ELKrNcxhhjjDHGlIQNh1xz3QQ8oKq/D6Q9JyLjK6oA6eSzOu2KigpHTq3x5KaNs3g1NV721gorizHGGGOqHmtxqbkaAd+EE1VVg59FJE1EJorI5yKSLyLvi8j5ge87icg+EbkmkNbQ559drktgjDHGGGOMZxWXmutd4GYRuUpEmsTJNw8YCPwJ6AMsA54VkQ4AqroEmAT8RUSO8NPcg9t3biqnshtjjDHGGFOIVVxqriHADmAWsFFEVonI7SJycCSDiHQDegG/UNX7VPVlVb0W+A9wW2Be44D/AX8XkQtx78tcq6pbKmhZjDHGGGPMj5yEeg6ZGkREUoEewLlAV+A44FPgp6q6Q0T+jGttOTw06W3AQFVtE5hXB+Bt4ADwT1UdFCPmYGAwQLOmTU954p7bomUrFztSD6N+/lcWr6bGa9mhbOPt2EH9+vXLdJ4Wr2Li1eRls3gWz+JVXrwuXbosV9WOFRLMlIhVXH5EROTXuJHGhqrq3SIyE7g2Rvb9qnpQYFoB3gNOwlV83ksUr/1Rh+vqAdvKoOTFk9N+PFmrK/BlcotXsfHK+OX8nJwcsrKyynSeFq9i4tXkZbN4Fs/iVV48EbGKSxVno4r9iKjq30RkInCsT/oO+BK4qBiTD/XTfQzcIyJnq+qB8impMcYYY4wxhVnFpYYSkeaquiGU1gxoCHzrk/4NDAd2qOoncebVHpgAjAFeBJYDw4Ap5VB0Y4wxxhhjirCKS831oYjMB14GNgBHAiOAXcA/fJ5XcD9Q+YqI3AmsAg4GOgBpqjpaRGr7/O8Bd6nqAREZB/yfiCyIV+EhpS5kf14+SxdNTg70q8Df+rB41TueMcYYY6oVq7jUXLcDF+KGLm6M+02X/wCXq+o6/p+9O4+zc7z/P/56J40kDFUiBCFURYuW2qqqJrSWamundBH1lRZVKqhSzYT+1FJraStUU7Wmat9DDGLfl6qgBEEsSYWQRZLP74/rnubOcc7MnMk5Z05O3s/HYx5nznVf9/W57nOSmfOZa7lJ93SRtCtwDGkq2Gqk6WNPAH/I2jkKWB/YIDc17NSs7b9J+mpEzK3NJZmZmZnZ4sqJS4OKiHOBcztRbxZpu+Oiq6Ij4nfA7wrK5gKbV6CbZmZmZmad4vu4mJmZmZlZ3XPiYmZmZmZmdc+Ji5mZmZmZ1T0nLmZmZmZmVvecuJiZmZmZWd1z4mJmZmZmZnXPiYuZmZmZmdU9Jy5mZmZmZlb3nLiYmZmZmVndc+JiZmZmZmZ1z4mLmZmZmZnVPScuZmZmZmZW9z7V3R2wxjXj47kMOvrGmsUbvv4chjreIh9v4kk71iymmZmZLTo84mJmZmZmZnXPiYuZmZmZmdU9Jy5mZmZmZlb3nLiYmZmZmVndc+JSQNL+kkLSqgXlJ2flPygo/2ZW/tWFjNuctbNed7ZhZmZmZlaPnLh80n3ZY2Ei8lXgoxLls4BHq9wvMzMzM7PFlrdD/qTngKmkhGQMgKRewMbAaIonLo9GxKyuBJMkoHdXO1vP+jKLCX32qVm81h4jmdhnhOMt6vFacoUt02oW38zMzOqbR1wKREQA97NggrJh9vhHYD1JSwNI6gFsBtzbVlHSzyS9IGmWpBcl/SLfvqQWSe9K+pqkh4GZwB7F+iLpe1k7B+bKvijpeknvSZou6SFJ3yx1PZKGS3pY0jRJb2XnrlVQ52uS7pH0fvb1hKQ9cse/K+lRSR9K+q+kByVt1e4LaWZmZmZWQU5cirsP2EBS3+z55qSpYM8A00jJCsC6wKfJEhdJBwB/AK4DvgP8AzhN0tEF7S8J/A24ANgeeKiwA5L2Ay4CfhIRf8rK1sliDQB+CuwCXA0MbOdaVgXOAXYCDgB6AvdJ+nTW5jLADcBLwG7A7sDfgWWz458FrgTGZdf0/az+cu3ENDMzMzOrKE8VK+5eoBewCXA3afTl/ogISQ9kz29n/qjMfdnoSwswOiKGZ+W3ZQnCrySdGREzs/K+wOERcW1bQEkDct//FDgL+FFEXJ7r1whS4rRlRMzIysa2dyER8b8RH0k9s/pvkxKZi4C1ScnXzyLig7Z+55rYEPggIo7Mld3UXkwzMzMzs0pTmhlleZKWJCUIx0XESZJeAw6NiKskHQdsERHbS/obsHlErC1pNeAVYMeIuCnX1lZAK7BpRDwsqQX4DdAnImbn6jUDdwLnAz8CvhcR1xT06y3g4lxiVNjvtjbWj4hnsrKvACcAX2bBUZL/FxG/lvQZ4NWsjxcAd0XEe7k2BwP/Ai7Jvu6NiA/bee2GAcMAVujXb6MxZx9bqmrFTe+9Mk2z3nC8Roo3YIPqxZs+naampqq173iNEcvxHM/xFp94Q4YMeTQiNq5JMOsSj7gUEREfSXoC+Gq2LfKqzN9t7H5geLao/qvA+Ky8bcTkrYLm2p7nk4b/5pOWArsBLwJ3FDm2PPBmZ68jS6ZuI01F+wnwBjAbuBHoAxAR/83WyLSQNiPoIek24JCIeCkiJkjaCTiaNNLysaSrSYncO4UxI2IUMApg8JoDo3lCDRd3Dx6J4zVYvL2rtzi/tbWV5ubmqrXveI0Ry/Ecz/EWr3hW37zGpbR7SWtbvgpMjIjJWflDwNJAM7AW8xOatoSif0E7K2aPU3Nl7Q1zfR9YCrhOUp+CY1OYnyB1xvak9TQ7RcSVEXEf8AQF61Mi4oGI2J60rmVX0vSxS3PHb4yILUmJ0/7AN0hreczMzMzMasKJS2n3Af2AfUmjLABExPukqVNHZEVtO4pNIo1oFO4QtifwPvB0J+NOArYhJQ//zLZibnMHsGeRhKaUvsA8YE5Bf4qOtEXEjIi4HrgQ+EKR49Mi4lLShgCfOG5mZmZmVi2eKlZa20jKDsChBcfuJ+3Q9V/g3wARMS9bv3KepCmkRfBbAQcCx+QW5ncoIl7Kpm/dBVwsae+ImAeMBB4G7pZ0GmkEZkNgSkRcWKSpcaRdxP4q6S+kXdCOAPJrWHYEfgxcQ1rrsgppWtm47PhPSCNPt5ASs8+RkrOLOryQXktCy2udveyF19pa1alFjtdg8czMzGyR4hGXEiJiEumDvMiNuGTubyuP3O4GEXE+KcnZhbRl8N7A8Ig4qQvxnwW2BbYDzpekiJgAfA14l7SQ/mrS9sWvlGjjaWAoafvmG4B9SElH/tPhi6SpayeS1sOcQkpSfpwdfwpYATg9O/5r0gYCvyz3mszMzMzMusojLu2IiNVLlI8GRpc49gfaWf8RES0seG/wtvJWUjKUL3uc7H4qubKngG+VaLtYG38n3Zclb1Du+ARS8lOqv/cDO5Y6bmZmZmZWCx5xMTMzMzOzuufExczMzMzM6p4TFzMzMzMzq3tOXMzMzMzMrO5VJHGRtGzHtczMzMzMzLqmrMRF0oGSjso930DSJGCKpEclrVrxHpqZmZmZ2WKv3BGXQ0h3gW9zNummhN/P2ir7fiVmZmZmZmYdKfc+LqsBEwAkrQBsAWwTEa2SZgPnVLh/ZmZmZmZmZY+4zAKWyL4fAnwE3JM9n0rBzRLNzMzMzMwqodwRl4eAg7N1LT8HbomIudmxNUnTxszMzMzMzCqq3BGX4cC6wNPAQODY3LG9gHsr1C8zMzMzM7P/KWvEJSKeBT4raXlgakRE7vARwORKds7MzMzMzAzKnyrWZiqwqqSBwJMR8WFEPF3BflkDmPHxXAYdfWPN4g1ffw5DHW+Rjtdcs2hmZma2qCn7BpSSDgJeB14hLcwfnJVfJemwynbPzMzMzMys/BtQHgmcDpwPbA0od7iVtM7FzMzMzMysosqdKnYw8JuIOEVSz4JjE4C1K9MtMzMzMzOz+cqdKrYS8GiJY/OAPgvXHTMzMzMzs08qN3F5EdiqxLGvA88uXHesMyS1SIrc12RJN0j6Ynf3zczMzMysGsqdKnYm8EdJs4Ers7L+kvYHDgcOqGTnrF3TgO2z7wcBxwNjJX0+IqZ2W6/MzMzMzKqg3Pu4XCDpM8BvgJFZ8U3AR0BLRFxa4f5ZaXMi4oHs+wckTQTuJyUzdfE+9GUWE/rsU7N4rT1GMrHPCMdbVOOtcm3NYpmZmdmip+ztkCPiVGBlYAfgB8C3gFWycus+T2aPAwEkLSXpHEkTJH0k6WVJ50paJn+SpJ6SfiXpeUmzJE2SNLqgzk6SHpE0M5uWdoqkXrW5LDMzMzOzMkZcJPUBrgNOjIhW4LZqdcq6ZLXs8eXscUmgJ3As8A4poTkW+AewXe6884AfAacAdwHLAbu1HZS0J3BZVu8Y4LPA70hJ7xHVuRQzMzMzswV1OnGJiJmSNiF9GLY6IKnt/VsdOAd4ArgWICLeAQ4sqPsyMF7SahHxqqR1gP2BQyPi7FzTV2TnCDgVuCgiDsq1NQs4V9LvImJK1S7QzMzMzCyjiOh8ZelvwPsRcUj1umQdkdQCFC4+mAJsEhEv5+r9kLRpwueApXJ1vxkRt0s6EPgj8OmIeL9InMHAc6TpgGNzh1YlJUHNEXFXwTnDgGEAK/Trt9GYs4/t0jV2xfTeK9M06w3HW1TjLb0WTU1NtYs3fbrjLaLxGvnaHM/xHK/74g0ZMuTRiNi4JsGsS8rdVexW4FRJA0iL8t8CFsh8IuKmCvXN2jcN+AZpBOxLwO+BSyVtERHzJO0CXAT8iTTFayowALia+ffbWR74sFjSkumXPZZ6TwcWFkTEKGAUwOA1B0bzhBou7h48EsdbhOM1X0tzc3Pt4rW2Ot4iGq+Rr83xHM/xujee1bdyE5eLs8dds69CgaeS1cqciHgk+/5BSTNIicoepKleewAPFkzxKrwHzxRgKUnLlEhe2rZVHgY8XuT4y0XKzMzMzMwqrtzEZY2q9MIq4WLgl9nXFUBfYFZBne8XPB+XPf6ItEam0ATgdWBQRJxfua6amZmZmZWn3Pu4vFKtjtjCiYiQdCJwiaRtSGtSzpV0LPAgaZ3KNgXnTJA0CjhNUn/gbmBZYPeI+F425Ww48PdsG+WbgdnAmsDOWb2PSnaq15LQ8lrFr7Wk1lbYe5rjLcrxzMzMzEooK3GR9IWO6kTEs13vji2kK4AW4ChSorImcChpTctYYB/ggYJzDgJeAf4POBp4m9xW1xFxhaT3SetkfgzMBV4CbiAlMWZmZmZmVVfuVLFnKFiMX4TXuFRZRLSQEpTC8rnA2rmiI/jkvVZU5JwTs69S8W4mjbaYmZmZmXWLchOXIUXKPkO6oeF2wM8XukdmZmZmZmYFyl3jcleJQ9dI+i2wJ2kKkZmZmZmZWcX0qGBbdwI7VbA9MzMzMzMzoLKJy47AexVsz8zMzMzMDCh/V7ExRYqXANYBPkfaecrMzMzMzKyiyl2c359P7io2E7gHODwibqpIr8zMzMzMzHLKXZzfXKV+mJmZmZmZlVTWGhdJF0pao8Sx1SVdWJlumZmZmZmZzVfu4vyhwAoljvUD9l2o3piZmZmZmRXRlV3FCte4tFkPeGch+mJmZmZmZlZUh2tcJB0KHJo9DdLNJmcVVOsDrAiMrmjvzMzMzMzM6Nzi/GeBfwICDifdaPLNgjqzgeeAYtslm5mZmZmZLZQOE5eIGAuMBZD0AXBBRLxe7Y7Zom/Gx3MZdPSNNYs3fP05DHW8hTbxpB2rHsPMzMysXOVuhzyyWh0xMzMzMzMrpdwbUCJpc2B/YG3S2pYFRMSmFeiXmZmZmZnZ/5R7H5dvAncDqwJfI+0iNh34ErA88EylO2hmZmZmZlbudsjHA2cBbZPgj4uIrUmjLx8DrZXrmpmZmZmZWVJu4vIF4GZgHmlr5KUAIuIVoAU4tpKdqyYlQyU9KGm6pPcl3SXpu93dt4Ul6ShJzUXKQ9LPuqFLZmZmZmYLpdzEZSbQIyKCtCXyZ3PH3idNIVtU/BG4AHgQ2AXYC5gIXCvpl93Yr0o4CmguUr458I/adsXMzMzMbOGVuzj/SWAwaXvkO4BfSXqddB+X44GnK9u96pC0M/BT4MCI+HPu0M2SJgMnShobEY91Tw+rIyIeqGW8vsxiQp99ahavtcdIJvYZ4XgLqyV7HDwSWnZqp9606vfFzMzMLFPuiMuZpCliAMcAHwK3km5K2R84uHJdq6pDgReB84scOxH4APjflCpJu0h6SNIMSVMk3SRp9dzxL0q6XtJ72bSzh7KNDMimo4WkpnwQSRMl/T73vFXSlZKGZcdmSLpR0ioF550k6eksziRJl0haKd8uaaOEEVncaJs2VmyqmKSfSXpB0ixJL0r6RcHxFknvStpQ0gOSPpL0uKQtO/VKm5mZmZlVQLn3cbkp9/3rkjYC1gL6As9FxOwK96/iJH2KNGXqjxExt/B4REyTdCfw9az+D4GLgMuBEwABWwMrAK9IWge4F5hAGsWZAmwMDOxC9zYnjWgdTtpq+mTgGmCTXJ3+pOTqjawPw4FxktaLiHmkaW93AleSpsIBPFvitTgA+ANwOikBHQKcJql3RJyUq7ok8DfgDGAyMAK4StLqEfFRF67TzMzMzKwsZd/HpY0kAQOAlyNiTuW6VHX9gN7AK+3UeQXYXlIP4CTg6ojYO3f8utz3I4BpwJYRMSMrG9vFvvUHNo+IVwEkvQKMl7R9RNwCEBE/bqssqSdwPzCJtD313RHxuKQ5wKT2poZl19YCjI6I4VnxbZI+TZoCeGZEzMzK+wKHRcS47Nw3gcdJyd0tXbxWMzMzM7NOU1pnX8YJ0rdIH9Y3ICU+m0TEY5LOB+6KiIsr383KyaZVvUn6IH5WiTpnAj8BvkwarfhuRFxfou5bwMW5D/+Fx4cCfwWWjojpufKJwJURcUT2vBXoGxGbFWn/3Ig4Pnu+A3AcsC6wTK7qARFxQVbnXeCciGgpaCuAQyLiHEmrkRK0HfMjaZK2Im1rvWlEPCypBfhV1rd5WZ0lgFn5mLnzhwHDAFbo12+jMWfXbqO56b1XpmnWG45Xq3gDNqhsvOnTaWpq6rii4y328Rr52hzP8Ryv++INGTLk0YjYuCbBrEvKGnGR9CPgQuAS0q5cf80dfh7YH6jrxAV4l/She/V26qwOvE5aKwIp0Sll+Q6Ol+PtEmUDACRtQhrtuZo0EvQ2ac3RA6SpZeUYkD2+VVDe9ny5XNkHbUkLQETMTgNun4wZEaOAUQCD1xwYzRNquHh98Egcr4bx9q7s4vzW1laam5sr2qbjNWa8Rr42x3M8x+veeFbfyl2cfyxwakTsyycTlH+R7vNS17JpbfcDO2bTpRYgaRnSVsJ3k9arwPwP+cVM6eB423SrJQrKP1Okbv8SZW2J0S7AO8BeEXFdNhVscjux29PWZmHMFbPHqV1s18zMzMys4spNXFan9PqNmSw4damenQWsDfxfkWNHk67jHNKC+9eBfdtp6w5gT0mlRjwmZY+fbyuQtBnFX6svZ1O42uptQUosHsqK+gIfx4Lz+75fpJ3ZdDwCM4m0wH+PgvI9SffkWSS2tjYzMzOzxUO5i/NfAzYExhU5tjFpi+G6FxHXSPozcK6kLwA3kF6LvYChwK/a7uEi6SjgEkmXAJeRpmZtDVwWEY8AI4GHgbslnUYagdkQmBIRF5KSjteBsyUdR5qCdRQpOSj0DnCjpBHM31XssbaF+aSk8bBsDc71wFeBHxRp5znSiNItwHRgQkR8UPAazMvWr5wnaUrW9lbAgcAxuYX5XddrSWh5baGb6bTW1opPX3I836vFzMzM6kO5Iy5/Id0f5Aekv/5D2mBsG9KH8WL3RalXBwEHkLYgvpZ0R/k1gZ3yWwFHxKXAbsA6pC2GL8q+fyc7PoG0o9e7pO2HrwZ2J9u1LNsiehdgXnb+cFJy8N8ifboPOJd0v5y/AM8AO+f6chPwy6w/15ESjW8XaedI0j12biQlVRsVewEi4nzSPW12ISVvewPDC7ZCNjMzMzPrduWOuJxMuj/J34C2e6DcB/QEzouIsyvYt6rKpluNzr46qnsVcFU7x58CvtXO8YdZ8F4sAINK1P0z8Od22joFOKWgWAV1HgW+UuRcFSn7A+leLqXitTD/XurttmVmZmZmVi3l3oAygIMlnQ5sQ7onylRgXEQ8X4X+mZmZmZmZdZy4SLqNdO+PCbni1YFLIuLDqvXMzMzMzMws05kRl28An257kt2tfSxp6tNjVerXYicimru7D2ZmZmZm9arcxfltvL7BzMzMzMxqpquJi5mZmZmZWc10NnGJTpaZmZmZmZlVXGd3FbtV0pyCsjuKlBER/Re+W2ZmZmZmZvN1JnEZWfVemJmZmZmZtaPDxCUinLiYmZmZmVm38uJ8MzMzMzOre05czMzMzMys7jlxMTMzMzOzutfZXcXMyjbj47kMOvrGmsUbvv4chjZ4vOaaRTMzMzOrLx5xMTMzMzOzuufExczMzMzM6p4TFzMzMzMzq3tOXMzMzMzMrO41XOIiqUVS5L4mS7pB0he70FZI+lmZ52wr6bAi5aMlPVJuHyqhs7G7cr1mZmZmZrXQqLuKTQO2z74fBBwPjJX0+YiYWuXY2wK7A2cWlJ8A9K1y7IW1OfByd3fCzMzMzKxQoyYucyLigez7ByRNBO4nJTOXdkeHIuI/3RG3HLnXrCL6MosJffapZJPtau0xkol9RjR0PFo+3XHFlmnV74yZmZlZjTXcVLESnsweB7YVSFpO0ihJb0maKek+SZu114ikHSWNlfS2pPclPSBp29zxFmA4sHpuqtro7NgnpmtJ2kDSHZI+kvRfSZdIWjF3fFDWxp6SzpM0TdIkSSMl9cjVW1XSmKxfMyT9R9IJRfr/TUlPSfpQ0nhJ6xYcX2CqmKRWSVdKGiZpYtb2jZJWaffVNjMzMzOrsEYdcSm0Wvb4MoCk3sDtwLLAkcDbwIHA7ZI+FxGTS7SzBnA98HtgHrADcLOkr0fEvcAFwOeArYFdsnPeKdaQpBWAVuDfwD5AE3ASaUrbxhExO1f9FOCfpClo2wC/Af4FjMmOX0SahjYMeA9YE1inyGtwKvD/gBnZNVwhaf2IiBLXC2n62GDgcKAPcDJwDbBJO+eYmZmZmVVUwyYuktqubXXgHOAJ4Nqs7AfAesC6EfFCVv92YAJpxOTIYm1GxDm59nsAdwLrAvsD90bEJElvArM6Me1qePa4XUS8n7X5AvAAsBtwWa7u3RHRVn+spO2BXZmfuGwK7B0R12fPW4vEWw7YIne9PYCrSUnJc+30sz+weUS8mp33CjBe0vYRcUsH12hmZmZmVhFq/4/ti55sulbhwoMpwCYR0Tbichlp0f6WBfVGAWtExJCsXgCHtCUsklYljVh8AxgAKDvv3oj4Wlbn98DuETGooF+jgfUiYuPs+Tjg3YjYs6Dey8BNEXGwpEGkUaIfRsTFuTqXAqvlYo4njR79HhjXlmQUxP5aRKyVK1ublKh9MyJuL3G9rUDfiNisoL23gHMj4viC1w9Jw0gjP6zQr99GY84+trBK1UzvvTJNs95wvAEbVCbe9Ok0NTVVpC3Hc7xFNZbjOZ7jLT7xhgwZ8mjb5zSrT4064jKNlFz0BL5E+kB/qaQtImIe0A/4CvBxkXOLLqLPRiiuA5YmTdV6EfiQtGNZ/y70cQBpuleht0ijI3nvFTyfTZq21WYvUkJ1BrCspCeB4RFxRwdtUNBOMW+XKBtQrHJEjCIlgAxec2A0T6jh4vXBI3E8YO/KLM5vbW2lubm5Im05nuMtqrEcz/Ecb/GKZ/WtUROXORHRthD+QUkzSOtA9gCuAKYCj5DWtRSaVaLNtYANgR3yU6QkdXWL4zcpnvCsCDxaTkMR8TowNEuuNgVagOskrRYRU7rYvzbF+tif1H8zMzMzs5pYXHYVu5g0uvHL7PkdpETk1Yh4pODr6RJttCUo/0tsJK0ObFFQr3A0pJQHge0kLZ1rbxPSFLbxnTj/EyJiXra2ZiSwJGl9z8L6sqS2zQ2QtAUpcXmoAm2bmZmZmXVKo464LCAiQtKJwCWStiGNvvwUaM3WpLwELE8arZgcEWcUaeY5YBJwmqTjSFPGRgKvF6m3oqShwDOkdSwTi7R3OmnE51ZJJzN/V7GnSTuIdYqkTwO3Ztf0PNCbtPB/MmnHsoX1DnCjpBHM31XssU4tzO+1JLS8VoEudFJra8WmSTmemZmZWX1ZXEZcIE0RewE4KiJmAkOAsaTk4zbgLNJWxkVHEiJiFmknrznAlcAJwO+AuwqqjgFGk7Ywfpg0batYe+9kfZhJ2kHsXOAe0mL52cXOKWEmKdk5lLQG52/AR8C2ETGjjHZKuS/r25nAX0jJ2M4VaNfMzMzMrNMabsQlIlookixExFxg7dzzaaQP+4e205YKnj9MGpUno224AAAgAElEQVTJG11QZyawX5G2hhYpe5x0z5dS8Scyf+eyom1lCdUBpdpoJ/Yn2i683lz5n4E/txfDzMzMzKyaFqcRFzMzMzMzW0Q5cTEzMzMzs7rXcFPFrHIiorm7+2BmZmZmBh5xMTMzMzOzRYATFzMzMzMzq3tOXMzMzMzMrO45cTEzMzMzs7rnxMXMzMzMzOqeExczMzMzM6t7TlzMzMzMzKzuOXExMzMzM7O658TFzMzMzMzq3qe6uwPWuGZ8PJdBR99Ys3jD15/D0AaNN/GkHWsSx8zMzKxeecTFzMzMzMzqnhMXMzMzMzOre05czMzMzMys7jlxMTMzMzOzuufEpcok7SZpnKT3JM2S9Lyk0yWt3E39GSZp5+6IbWZmZmbWVU5cqkjSacAY4CXgh8C2wBnANsC53dStYYATFzMzMzNbpHg75CqR9B3gcGD/iLgwd+guSaNISUzdktQ3ImZ0dz/MzMzMzMCJSzX9AnisIGkBICLmAjcDSOoHnAZ8G+gLPAQcERGPtNWXFMAhEXFOrqwF+FlE9MueDwX+CnwROB34KvAacExEXJXVaQU2AjaStG/W1H4RMVrSROCfwHvAT4AVJe0E3AB8NiJezsVeA/gPsEtEXFvqBejLLCb02aczr1VFtPYYycQ+IxozXgvQXPKlNjMzM2t4nipWBZJ6kRKHWzpR/RpgO+AIYC/Se3KnpLW6GP5S4DpgF+AF4HJJq2bHDgKeA24CNs++8ndQ3AfYKqu3F3Ar8AawLwsaCrxdcK6ZmZmZWdV4xKU6lgd6A6+2V0nS9sAWQHNE3JWVjQMmAkeSRj7KdUbbKI+kR4G3SKM5f46IZyV9CLwTEQ+UOP/bETEz18fRwL6SRkZESBIpkbk4IuZ0oX9mZmZmZmVTRHR3HxqOpJWAN4GfRMSodur9Bjg4IlYsKP8rsGlErJs9L2eq2MCImJSr9wZwYUT8Onv+CPBMRAwtiDkRGB8RPygoXxN4EdgmIu6UtDVwB7BeRPyryDUNI20AwAr9+m005uxjS11+xU3vvTJNs95o3HhLr0VTU1Pt4k2f7niOV5fxGvnaHM/xHK/74g0ZMuTRiNi4JsGsSzziUh1TgFnAah3UG0CaclXoLWC5LsZ+r+D5bKBPJ899q7AgIl7K1sbsB9yZPT5ULGnJ6o8CRgEMXnNgNE+o4ZqTwSNp6HjN19Lc3Fy7eK2tjud4dRmvka/N8RzP8bo3ntU3r3Gpgoj4GLiXtHalPW8C/YuUrwhMzT2fBSxRUOczXe5gaaWG3y4AdpO0CrAraWTHzMzMzKxmnLhUz5nAxrndu/5HUo9sfcuDQH9JX88dWxLYERifO2US8Pn8+aR7wXRFOSMwba7Kzruc9G/m8i7GNjMzMzPrEk8Vq5KIuF7S6cBfJG0BXAtMB9YBfgpMjIhdJN0HXCHpaNIUsyNI2yKfmmvuauBgSY+Tbmb5f8AyXezac8B2krbL4r0cEVM6uJaZki4BDgYui4jC6WjF9VoSWl7rYje7oLUV9p7W2PHMzMzMFlMecamiiBhO2lb4c6RtiscCw0mL2w/Mqu2clZ8J/AMQsHVEvJhramR27LfAaOAJuj5d67fAv4ExwMPAdzp53jXZ4yfuS2NmZmZmVm0ecamyiPgn6caOpY6/A/yogzam88l7qQCMyNUZTUpqCs8dVPD8JeAbHdUrYlvgFWBcB/XMzMzMzCrOiYu1S9Jg4AukEaKRETGvm7tkZmZmZoshJy7WkfOAzYDrgLO7uS9mZmZmtphy4mLtiojm7u6DmZmZmZkX55uZmZmZWd1z4mJmZmZmZnXPiYuZmZmZmdU9Jy5mZmZmZlb3nLiYmZmZmVndc+JiZmZmZmZ1z4mLmZmZmZnVPScuZmZmZmZW95y4mJmZmZlZ3ftUd3fAGteMj+cy6OgbaxZv+PpzGLqIxZt40o4V6o2ZmZlZY/OIi5mZmZmZ1T0nLmZmZmZmVvecuJiZmZmZWd1z4mJmZmZmZnXPiYuZmZmZmdU9Jy4NTNLLkkLSWt3dFzMzMzOzheHEpUFJ2hwYlD3duxu7YmZmZma20Hwfl8a1N/Ah8Ez2/Qm17kBfZjGhzz41i9faYyQT+4xYxOJNq0hfzMzMzBqdR1wakKSewJ7AdcCFwOclfamgTrOkpyTNlPSwpE0lvSuppaDeTpIeyepNlnSKpF41uxgzMzMzM5y4NKohwIrA5cCVwMfkpotJWgW4CXgb2B04D7gE6JtvRNKewFXAQ8B3gZHAMOB3Vb8CMzMzM7McRUR398EqTNJfgF2BFSNitqQbgPWANSIiJJ0K7AcMjIgZ2Tl7AlcAIyOiRZKAicC4iNgv1/aPgXOBVSNiSpHYw0jJDSv067fRmLOPrealLmB675VpmvXGohVvwAadjzd9Ok1NTQsXrwyO53j1Gq+Rr83xHM/xui/ekCFDHo2IjWsSzLrEa1wajKQlSEnL1RExOyu+HPg7sDlwH7AJMLYtaclcV9DU2sBqwBhJ+X8n44A+pETorsL4ETEKGAUweM2B0TyhhmtOBo9kkYu3d+fXuLS2ttLc3Lxw8crgeI5Xr/Ea+docz/Ecr3vjWX3zVLHGswOwLHCTpGUlLQu0ArOYP11sJeCd/EkRMROYnivqlz3eRJpq1vb1clY+sBqdNzMzMzMrxiMujactOflHkWN7SDoMmAyskD8gqQ+QH4udmj0OAx4v0tbLRcrMzMzMzKrCiUsDkbQU8B3gMrLpWjkbAqcDWwMPA/tJ6pubLvbdgvoTgNeBQRFxfpc61GtJaHmtS6d2SWtrWVOvFrl4ZmZmZosxJy6NZSdgSeCsiHgwf0DSvcCxpBGZ44CDgeslnUGaOnY08BEwDyAi5kkaDvxd0jLAzcBsYE1gZ2D3iPioJldlZmZmZos9r3FpLHsDLxQmLQAR8TEwhrRw/11gR6A/abvjQ4AfAz2B93PnXEFKhjYgTT27CjgIeIyUxJiZmZmZ1YRHXBpIRHyng+MHkRIPgDuBL7Ydk/Q1oDfwZME5N5NGW8zMzMzMuo0Tl8WUpJNJi+4nA4NJ08eeosgWx2ZmZmZm3c2Jy+KrN3AqsCLwAXAbcHhEzOvWXpmZmZmZFeHEZTEVEYcBh3V3P8zMzMzMOsOL883MzMzMrO45cTEzMzMzs7rnxMXMzMzMzOqeExczMzMzM6t7TlzMzMzMzKzuOXExMzMzM7O658TFzMzMzMzqnhMXMzMzMzOre74BpVXNjI/nMujoG2sWb/j6cxhao3gTT9qxJnHMzMzMLPGIi5mZmZmZ1T0nLmZmZmZmVvecuJiZmZmZWd1z4mJmZmZmZnXPiYuZmZmZmdW9miYuklokvVvi2GhJj9SyP9YxSYMkhaRvd3dfzMzMzGzx5REXMzMzMzOre4vlfVwk9QR6RsTs7u5LnqS+ETGju/tRKX2ZxYQ++9QsXmuPkUzsM6JG0abVKI6ZmZmZQZ2PuEjaQNIdkj6S9F9Jl0haMXe8OZvGtF7Bea2Srsw9Hy3pEUk7S/oXMBPYTNKyki6Q9IakmZJelXR+B31qlXSlpGGSJkqaIelGSasU1Osj6RRJr0maJelJSd8qqDNR0mmSjpM0CXi/RMyRkp7PPV9K0seSHsuV9ZM0T9I3c2VbSrore/2mSDpf0tIFba8m6XJJU7N6t0oa3MFrMETSB5JObK+emZmZmVmldMuIi6RicVVQZwWgFfg3sA/QBJwEjJW0cRdGSwYBpwDHA5OBl4HTga8Cv8jKBgJf70RbmwODgcOBPsDJwDXAJrk6VwKbAiOA/wB7AtdlfX8iV28f4F/AQZR+P+4BfiNpxYh4K+vzHOBLkpaJiPeBLYF5wP0AkrYAbs/6tTuwPOn1+0z2HEnLAeOBKcBPgY+Ao4HbJa1dbPRH0nbA1cDvIuKETrxWZmZmZmYLTRFRu2BSC+mDfCmPRsTGWd2TSB+mV8s+mCNpM+ABYJ+IuExSM3AnsH5EPJOL0wq8GxFtH9BHA/sCG+aTBknPAOdFxB/KuIZWYAvgsxHxala2BSkB2CEibpG0DSlpaI6Iu3Ln3g28FRF7ZM8nAr2BNSJiZjsxlwLeA/aOiCslHQ+sS0qgfpzFPB3YMiI2yc65B5gTEUNy7WwN3NH2ekk6gZQwfS4ipmZ1PgNMBI6JiHMlDSIled8hjdCNAX4dEb8v0ddhwDCAFfr122jM2cd24lWtjOm9V6Zp1hu1CTZgA6ZPn05TU1Nt4oHjOZ7jdUMsx3M8x1t84g0ZMuR/n0OtPnXHiMs04BtFykcAA3LPNwVua0taACLiwezD/teAy8qM+3rBSAfAE8CRkuYCt0fE80XOK+axtqQl69e9kt7O+nwL6fomA/cWjC7dAQwtaOuO9pKWrP0Ps2lhW5JGcr5OGvWYk5XdkpXdDSBpSVJSc0hB/PHAx8BGwDNZP8cC7+fqfQA8ChT+x92NNDp0eESc205fRwGjAAavOTCaJ9RqzQm0Dh5JzeLtPY3W1laam5trEw8cz/EcrxtiOZ7jOd7iFc/qW3escZkTEY8UfpGmK+UNAN4qcv5bwHJdiFusrZ+RplL9Bpgg6QVJ3+tEW2+XKGtLvPoBK5GShPxXC2k6Wkf9KuYeYEtJSwCbZc/bypYGNsieQ5oO1hP4Y0H8WUCvXB/6AXsV6eeQIv38LjCVlDCZmZmZmdVUPe8q9ibQv0j5iqQRAUiL7AGWKKjzGaDwfjGfmBMXEe8BPwd+LumLwFHAJZKeiohn2+lbsX71z/oM6QP+68DO7bRRsl8l3ENai7MNMJs0WjQX+D0p0ehJGlGBNK0sSInSTUXaaptPNRW4Dii2VuWDgueHkNb03CZpq4goTDTNzMzMzKqmnhOXB4EDJS0dER8ASNqEtMi+7QP6pOzx88BjWZ2BwDrAC+UEi4inJB0JfD87v73E5cuSVitY49IfeCg7fgcwHJgeEc+V04923EPawOBo4N6ImCfpaWBGFuu5iHgnu5YPJT0ADI6I49tp8w7SpgH/6sQ2zO8D2wF3AbdK2jo/ja+oXktCy2uduLQKaW2Fvb1NsZmZmVkjqufE5XTgQNKH5JOZv6vY08A/ASJikqRHgBMkfUSa+nYMaSShQ5LGk6Y+PUMaoTgA+JD5CUgp7wA3ShrB/F3FHouIW7LjY4FbSTugnUzaNWwZ0nSuPhHxq870Ly8ipkp6lrSW5VdZ2TxJ9wI7AoXbOB8F3CFpHmldzAfAalndY7P1PKcDPwDGSfoDaZRoRWArYHxELLCOKCKmZNst3wPcIGn7iPio3GsxMzMzMytX3d7HJRs9GEKaDnYZcC7pA/M3C7ZC3ht4FbgYOJG03fGEToa5n7RY/krSbln9SDuDTWrvJOC+rD9nAn8hJT7/mxYWaau2XYELgcNIScx5pAXz4wsbK0PbGpa7i5Qt0G5EjCclOSsAfweuJyUzr5Gtq4mId4GvAM8BZwC3kbaM/jTwVLEORMSbpOlqg4CrsjU3ZmZmZmZVVdMRl4hoIa27KHZsaJGyx4GtO2jzRaC5oPjajtrOyo8Ejmyv/Xbi/hn4czvHZ5F2Siu5zVVEDCoz5oGkUah82cmkEZ9i9R8Etu+gzTeA/do5PpGCe+xExCuk0RszMzMzs5qo2xEXMzMzMzOzNk5czMzMzMys7tXz4vy6FBHN3d0HMzMzM7PFjUdczMzMzMys7jlxMTMzMzOzuufExczMzMzM6p4TFzMzMzMzq3tOXMzMzMzMrO45cTEzMzMzs7rnxMXMzMzMzOqeExczMzMzM6t7vgGlVc2Mj+cy6OgbaxZv+PpzGJrFm3jSjjWLa2ZmZmbV5xEXMzMzMzOre05czMzMzMys7jlxMTMzMzOzuufExczMzMzM6p4TFzMzMzMzq3sNn7hI2k3SOEnvSZol6XlJp0tauQqxmiWFpPWy50tIapG0QRfbmyjp9x3UaZV0ZVfaNzMzMzNbVDR04iLpNGAM8BLwQ2Bb4AxgG+DcKoR8DNgc+E/2fAlgBNClxMXMzMzMzJKGvY+LpO8AhwP7R8SFuUN3SRpFSmIqKiLeBx6odLuLqr7MYkKffWoWr3WVa5n4/eaaxTMzMzOz2mnkEZdfAI8VJC0ARMTciLi57bmkkyQ9LWm6pEmSLpG0Uv6ctmlbko6TNDmre4mkT+fqLDBVDPgge/xrVh6SBnU2ZqVI2lrSg5JmSnpL0h8lNeWO98qu7dVsOt0bkq6WtER2fFlJF2TlM7N651ejr2ZmZmZmxTTkiIukXsBXgdM6eUp/4ETgDWAFYDgwTtJ6ETEvV29v4EXgAGAAcApwAbBHiXa3BsYBvwXabiH/ZpkxF4qkdYFbgLHAbsBA4CRgTWD7rNqvgO8DRwMvAysB3wJ6ZsdPJ72evwAmZ218vVJ9NDMzMzPrSEMmLsDyQG/g1c5Ujogft30vqSdwPzAJ+Bpwd65qX2DHiJie1f0Q+Lukz0fEv4s0/XD2+J+IWGAKWRkxF9ZxwCvAdyNibhZvKnCFpM0j4n5gU+DSiPhb7rwxue83Bc6NiCtyZRdXsI9mZmZmZu1SRHR3Hyoum3L1JvCTiBjVifo7kD7grwsskzt0QERckNWZCDwYEXvlzlsS+BDYNyIuktQM3AmsHxHPZNOxPgD2i4jRXYx5ZUQc0U7fW4F3I2L3Esdfyto4KlfWE5gJHBMRp0r6LXAgcDJpdObpyP3DkHQxaYTlJOD2iHi+nf4MA4YBrNCv30Zjzj62VNWKm770WjQ1NXVcsVLxpk93PMdzvG6I18jX5niO53jdF2/IkCGPRsTGNQlmXdKoIy5TgFnAah1VlLQJcB1wNemD+dtAkBbZ9ymo/nb+SUR8JGk6adpYp5UZc2ENAN7KF0TEXElTgOWyot8C84CDSMnL65JOjYizsuM/A44HfgOcK+lF4LiIuLwwWJYojgIYvObAaJ4wosKXU1pr87U0NzfXLl5rq+M5nuN1Q7xGvjbHczzH6954Vt8acnF+RHwM3Ats14nquwDvAHtFxHXZlK7JJer2zz/JRlyamL9upbPKibmw3uST/e5Jmk43FSAiZkbEbyJiELA2cAVwpqTts+PvRcTPI2Il4EvAg8Alkr5QpT6bmZmZmS2gUUdcAM4ErpO0b8HaDST1ALaNiFtI61Y+zk+NIi1UL+abkpra1riQEpAAHilRf3b2WDiKUk7MhfUgsIukY9rWuAC7kt778YWVI+IFSUcABwNfIE0dyx9/StKRWX/XAZ4tGbnXktDyWkUuolNaW2sXy8zMzMxqqmETl4i4XtLpwF8kbQFcC0wnfdj+KTCR+bttHSbpTOB60u5ZPyjR7AzgRkmnkqZgnQpcHRFFP7xHxGxJLwN7SnqGtK7kqTJjdsYqkj6xxiUiriRNA3scuEbSn4BVSdPBbs0W5iPpauDRrN4MYHfSv427s+PjSdPaniElageQ1vY8tBB9NjMzMzPrtIZNXAAiYrik+0hrNC4ljXRMJK0v+X1W5yZJvwQOIX0gvx/4NlBsAfrlpMX2fyFNEbuOtKi9PT/NYt1O2ulsjTJjdsZXgH8UKVdE/CvbCOBE4CrgfeAy4KhcvfuAvYAjSdMHnwV2i4i2kaT7gaHAIGAuKcHZISImdbG/ZmZmZmZlaejEBSAi/gn8s4M6p5DuyZKn4lWjBWgp0U5r4XkRcRvwxa7EzNactCsimjtR5w5gs3aOn0oaPSp1/EhSUmNmZmZm1i0acnG+mZmZmZk1FicuZmZmZmZW9xp+qlildGbalpmZmZmZVYdHXMzMzMzMrO45cTEzMzMzs7rnxMXMzMzMzOqeExczMzMzM6t7TlzMzMzMzKzuOXExMzMzM7O658TFzMzMzMzqnhMXMzMzMzOre05czMzMzMys7jlxMTMzMzOzuufExczMzMzM6p4iorv7YA1K0gfAhBqG7Ae863iO53gNH6+Rr83xHM/xui/e4IhYukaxrAs+1d0dsIY2ISI2rlUwSY84nuM5XuPHa+RrczzHc7zuiyfpkVrEsa7zVDEzMzMzM6t7TlzMzMzMzKzuOXGxahrleI7neI63iMdyPMdzvMUnXq2vzcrkxflmZmZmZlb3POJiZmZmZmZ1z4mLmZmZmZnVPScuVnWS/O9sEdbo758kdXcfrHyN/r4tBtfXW9KG3d0P65pG/71g9cv/8KxqJPXMvu3bTfGbJG1Qgzjd8v+o2teX++DUu1ox2ondJGlHSVWLLalH9m902WrFaCd2td+7vpK2qObr10H8al/f0sCZklauVowO4lf7+pYCzpa0ZbVidBC/Fu/fvcCh2fOaJWm1+L0gqZeklSV9rppxSsSu9nvX9vtuiWrF6CB+TX6v5+L5c3Kd8RtiVZH9YrpU0njgMUnH1viHzdLAJGC4pIrfaDX74fldgIiYV+sfbjW4vqWAEyRdC4yV9P1qxCkRe2ngFeCHVYzRBJwH3A08KennkpasVryC2NV+73oDjwNjgGZJvSodo4P41b6+ZYBngTWBDyvdfifiV/v6lgYeArYANmvAny3LAI8C6wN7SPpK1GiXoGpfWy7GNcA4YIKk0ZK+XI1YJWJX9fceMErSHcA4ST+T9NlKx2knfi3ev96S1pH0FUm9ImJeVt7QI6CLEicuVnGS+gIPACsAd5F+iB8JnCtpWA3iLwM8Sfrl/6uImFPh9pfM2r5G0k+gtslLDa6vKWv768AcYCrwd+Dw7HjVfoDnru0x4IiImFWFGEsDjwBrkT5AXQ+cCQytdKwisav63mV6A0sBA4DzgW/UMOms9r/NpYEngAnAgRExrZLtdyJ+ta/vU6SEczKwG3BW2wen7HhVPzzV6P17EngV+DbpDxQHZX8oqapa/N/LfjeMBz5F+pnyU2APGuBnS/YePQKsA0wE/g2cDVwsafdKxioRvxbv39JAK3ALMJaUeB4maY2ICCcvdSIi/OWvin4BewIvAGvmyjYFbgCeA46sYuwm4CXSh9FVgR65Y6pA+58CziD9wr0DmAcckjveY2FjdPP1LQFcBdwGrJGV9QRGArOBDap4bUuRfiFeCwxsu7bsNe8J9KxAjD6kv4TeDgzKlY8Cxi/K713+3x/wJ2A4cCXwDrAD8KlF+fqy9l8G7gRWypUvS5qOusKifH1ZOyuTkunv5srWIY2+bAIss6heH7AM8BrpA2H/rOwMYFruZ01Vfn7W4r3L2joKeAZYo61d0h98XgN6L6rvXe46JrDg7/UdgI+BN4H9F/HrW4KUtNwKbAt8Gfhr9jPnerLffZX89+Kvrn3V5K9wtthpIn1AfB/SXxEj4iFJRwDHAP8naXpE/KmSQbMRj7OBQaS/1k/Kyrci/SBaV9LDwD0RcXcXw6xMSszuA44l/UXtLElExB8iG3mJ3F9JK6VG17cp8DngFFJyRkTMlXQVcBDwedJfvCsq+0vWr4HVgBER8VpWvg3pL8/rAs9Luj4irluIUFuSPkCNJLu+zOvAEpK+RRpleiYi3liIOAuo0XtH7t/dG8DOwE7AFaRfwPsDN3a17fbU6Pq+BaxOSqxnZ+3vQBrNXTM91Z+AyyLilZKtdEGt3j/Sv83VSe8f2V+y235OLg/cJenCiPj7QsT4hGpfn9JasrOAF4F9Sck0wInArsD/A/ap0s/NntTmvQP4LDA9Il6WtATp3+lk4F/Aj7I/2P87IsYvZJz/qeG/zdWA9yPipaz9XhFxs6T9gIuAIyTNjIhLFiLGJ9Tw+lYi/cHsyIi4LSvbT9KBwH6kGSMHR8QTkhQRvglid+nuzMlfjfcFbEf68Pf17HlP5v/1aTBp6tgDwFeqEHtP0l9kHwPWBpqBmaS/gj1F+iXyBvC9hYixK7Bc9v0apB+qhSMvCz060B3XR5oediHQlD1X7ti/gdMLyyt4bVsC/wCmkBKoLUi/+B8gjZL8O3udD1qIGCsBuwB9cmV9SH9JfJuUwPz/9s48Xq6iyuPf87KQFYggGEhINBAzEhEUghESNtkUYWBQVETyGVFgJJGZ0Ux0hohkFGXfHMEgxIgsiuKEVbZkEGQJayCBgJiEgKwSkpCESHjHP071dL2b1/26b9/uvt2c7+dTn/e67r1169dVt24tp053AjcBe7ZS2cXlAuyKmaz0AgZgq4MvAfsDV2MdgJaqm+Ee3wTeAU4Gjg7/XwOcA1wcyu5KYNsW1TcCeA0bcL4HWI0NsncH9sVMbx+s5R7N0hfq5JBEXF+s/VwG7BbX4Yy1fb7eZRfu822szRoVPg/CBi0vAvdjA7YFwFdbqezCPaZgq2MfCJ97AQLsGersw9jKxNatVjfDPcYCK4B9w+f+0bEvYCZqNxXK1kPzQtMz4KH9QngZzcHsUbcPcR0UO1UfA14GTg+fMzHhiv4/LLzglwFvYqs8w8KxvTAb8uXUOHAiDE6wmajuBi9CRuY5dB1AHFpPfQSTm0hf7/D3buCiUvmq9XsM/48DrgsvpTeBaYUXIWYycwE2KD6o1u8ylE8H8AdgPjAe6+Tvhw2eZmehMaGvUXVzYEhnUvjcDzOBWIuthKb+/nq4b13rZkjnP8KzthYzzRkUHTsiHDs5Iz2S+NwIfT8FnsYGab8n6uwDOwP3Yma3Hcn85VFfT3nE9pqtAc7MuC5uAnyswWX3oXCPzlB2L2ADljHR8Vswj2qZdvCxFdZ6vhfGYQOHX9HVXGwi9m74ImY29rkMNTX0vY6Zad4Ufe4T/f81bMXwO0STsR4aH5qeAQ/tGYAjMZOiq4ERIa4XxU7wVGyGZItaGgCKneuBifgjQiP3E2DLxLF9sI5p6pn7bvIRD16+HuJGAccBO2SQfpdOStSIZ6avVDlEZfYb4IoofhBwVFp9FAcQ/RPxuwNXYSs/QxPHPoTZOv8wo3LrDUwGtknEn4jNnA6rscx6sfEsc6GDUZe6GT0TPwdOieJ/iw0IX8ds02vaT4DtK9mDhO1+PepmN/c+CVuF+2AU1zf8/QU2E7tpjW3LYMy8KVk36s4hgvYAABaHSURBVKoPM3+5D+v0PkJxZrvwHB4a2pma2hVscHshMKHR5RelV9iT9X1s1n58RukWHHDMakLdfD/W7v9TKL9j6Dpxt08ovwk13KMPZra8A10nRurdtnwDW+F4KDyDU7CJkJnh+CXABRnWi36J+Lq816P7HY0NfqbH33X0/08wr2aDs6inHlLWj2ZnwEN7Bbp2rqdim/GvIqy8RMcmYy5N+9dwr8GYmcjdmKnPf9J1hu1TBHO18Dlu4JcDF2esfSTW0ekEpoe8rSal2Qo2MIg36SYHLwc0WN8VmMlRR/juL8NmSqvu3GOdpv/GNuLfHV4YfaPj44DdY+3R/wuAqzPQU7Ljjpl8PJl8cVZZdjOxmdXnwgt+cHS87mWH7Ul6BFtFuoaiCdLN2Mzo/jWkvUl4tl/ATEP7JI43Qt8O0f/xc3EFML/GtAsbya8HNuvmHnXVh22EfhFYF98nHDsUm3UeUUP6gzETpoexlZ2OxDN2YIPblr2wVfjTkvdLWXZPA+tD+/TxxPG6lB0br169F5skOCB87sAGoAeG+6RydBLK7sbw/HUCswhmdvXSl6gbX8YmsVaHOno+YfIitC01tc1Y23kp9q75IzZAGh0dr9t7HdgK2w/4OPCNKL5/+DsMG6h9Jot67yFlOTU7Ax5aNxCZ3CTi40ZuGkUXhh/HZri3CI3DH0g5c4HN9i7EZl2/D/wIeAPrKJ7YzfmFWWjBNno/A5yQRl8P1wzHOvSd4aX10ZT6BmADu07g+Pi77S4/9dRHcTbqf0KZDcRm1lYRDRSr0Faw+74rvADnBJ1TS5wfa9sBG7hMS1s/y50b/t8KWyn8JWZiVdWsPdaxeAqzyb4A+DFl9ubUq+ywwd8j2AzlKxQ7UIOw1ZfR1ehKpF3o2HdiA7OSXsvq/ezR1ZxkKOYR73LMZLXqFZdQfn/GPM8NSxzbqEOdpT66tp1TQrmtxjqjg7D2pdCxek/KsuuNdTDvwFYH+vRwfqPazouwPQZDK72mRNktCWW3P9aGzsbarI0mKtJoq1Qf1lbfHr7r4SFuK2xC4zFSeMHD3guPYWZoJ2DmS2soscpRr7oZPo+gq8nYsJCv1PvnQjk9hU1m/SyETsw88qisyq8H3e/H3kmPA99JnLMr1t59Iq1GD7WHpmfAQ+sFih3Zku4dEy/gL4bGewNmwvEotqT7kRry0JPL5alRfLzU+77QGD5PcMGZRl+ZfA0PjfcK4B9SaqvK5XLiu85cX/RymI6ZCFyBzQTvkkJbVe6WE9qGB20vU2aDZI3lNxab7XuFYJde5fVVuVuud93EBi0rgE8mvstaTKgqdrnc4Gdvp5D+62nKLqRRlctlug6aMtGXKKcjw7PSidnXPxnqZmq35PTscnnzRpYfxQ7jWKwT/qOUuipxt9wrmddKtaXRhw0+F2ETNVeHevUi8OGUGit2t9zgZ28XbDLrFWowYaRnl8vHpdXXndYydfEDmMnpUmzP5VhsL8+PQ9w21WrzkF1oegY8tFbAZrRmYp2zGzGb14HR8XjmuiNx3RHAKdjSb02eOYB/Do31luFzwf57DDbDVviBuvia0zDTj5cp8eKvRl831w7AVlvWU9ugbDvMBOfXmAeVc6jg92IaoO/kkI/VpdKvQNue2EzWMYn68RGs8/uFEtf9APPo8mK5e9eobzLW6flz2vLDZnkfBD6TeBa+i5l0fAqbPU/um8i07Ch2QvpiHfrMf8MlPMt/wLxf3YZ59vl0iXPrXTePx1bwnqvx2ftcqONnU/QceHDI01JsMmEaCTOtepVf+H9AqE/fwvZO9Ngx60HjGMxscNfw+cjw7L0atM8Fjmlk+YXjfbBJg6pXArHJj8tD3reh2AF9byizK8tc26O2FOUX//+FkLd7MM93qQbVIa1LgPvC/4U9XV/ENvx/NYQ9q9VX47P3Qez5X1LLsxfSOo/IzJMwOAG+FOrmk8DRKctvAHBYd89Y4rxC27l1eN4WYoPf5Vi/ouoJOw/ZhqZnwEPrBIrmS/OwGeTZ2CrK7UT28tFLo24/xkgKl8uYSdktlFgJyUIf5pFqbAb6qna5jHXu66YP+6Xru0qlX6GuVO6Wsc7iNUSbseug70uE3wSpQV8qd8t1qJt1cceduEfFLpfr/exhne9pZOCqlBQul+utL+PyG0GVLpcbUH5lzdUq1JXK3XJP2rIqv5CXmp5LUrhbbkTdxCakRmZQhlW7XK6i/B7BvJHF303ZlZcoDxOBD1MHV88eUtSTZmfAQ+sEYBJmnjUqitslvBQeIrJBpeuM4YfqkJeqXC5H1w0qk2Zu9EVpV+JyOV4yL7lnqAZ9O0b/D0mjI5GPatwtx6YdA+pUP8dG/9e8MhHVwYrcLbdw3azK5XKd9MVlV3PnN0qrapfLrVR+VO5yOX7+cqmPnldyenS3XE5bVvUzo3Krxt1ybOqYy7LrJi+pXC73oK83Ztb6GrbXdiHRPhg2HkTXbdLHQ0b1pNkZ8NA6AZvteSb6XFjG3S40cA+xcWdlJmZ6s0cd8lONy+UeG6O86esmf+VcLpdcichI38QM8p/W3XJFphU16sv0xyZjbZR3tzw8+eJshbpJcdCZicvlnJZdpS6XW65toXKXyxWZbeVNX3SPsu6WqXCvV570kbG75TxpC2lX7HK5kvYFM7d+ClvpH4e1UYsoM3gJcccQ9kp5yFfowHEqZwEwTEQmAKjq2yLSW1Wfw8xjhgDTRGSL6JoHsQ7ac1llQkQk3P9azGRjZ+CHIrK9qr6jqhvCqeswT2NrVfWdCpLOhb5ShHycg/32woUiMh0zDzsXWwLviVr0Lckg/1oiPi6voSLSISKDsUHaZVSmDWrTt6xqQT0gIh2qukFVL1TVvyQObw48C7yqqp0VJJeruhk9T/cDR4jIABG5BpsZPSrEz8FWlyohV2UX8nAR5tFvMVi7o6p/C4cFWKeqq1qxbVHVW7FZ7Q7Cxnw1NkSnLcf261VCrvQViJ6tWzGTv4MBRKRXON5tm9QNTdcXvfeWqOqlmNnuCODloFPCOX2xAenqCpNuujYs8x3h/ucDZ2F7yk7HBlaXY4MYsAHVVuHcStrO5SG9b6rqA8AMbLVxioicUEinUCdCXiZjkzJfL+TLyRHNHjl5aJ2A2UP/EbOBHRHFF2bpxmKzrd9OXJf6x5pooMvlPOnr4ZpULpcbra9SbRRnRWtyt5xXfclzSOFuOa91k4xcLudVXzIf4f+qXS7nSR9d285MXC7n+dmLrkntbjmP+sjI3XJe62b4PIIMXC5H9yusJn0E2webXHmJ2+lLyODHoz1kH5qeAQ+tFbCOyhuYJ4+toviCCcW5mH3tZlHDl+a3FJricjlP+spcm9rlciP0VauNbN0t505f4trU7pbzWjfJyOVyXvUlrk3tcjlP+hLllInL5bw+exQ7rbW6W86dPjJyt5ynulnm2kxcLkfpxYOXr4W4kSQ86nnIX+iN41SBqj4gIkdiHWcVkUtVdbkWTSjWYK4V12owO9DQIlRKMBM6R0RGAetE5FbgUlVdE46LGp3BHKdTVa8Ukesxj0Y7Yh2pm1X12VbVV+LaAZgHoL2Bcar6ZJ70pdGmRVObVdjLaTRmi/5oNdryqi+6djK2d2AU5q3nqTxpq1Zf4dnDnrkxwCKNTDeqvXfe9HVz7fGYl7GRwD6tXH6JtvNaEbkJM+sbg7Wdd6jqkjzpS1t2Udxi4Cps4Fk1edIXleMFIvIqtlo2GlslO7GV62aJaz8InIE5WthfVZ+p5t7doaqPicipmKv6k0VkS8ypz+EicpuqvlTrPZw6UWpE48FDuYDNrq7CZo/Hh7gtMbvQG+nBA1SZdHPhNjTP+sjA5XI99NWqjQzcLedcX83ulnNWN+vifSdH+uricjmv+vJcfhmUXZYe53KnL8Rl4W45t3WTjFwuR+kVVn92wqw1OjErjYpMrz00LzQ9Ax5aN2D7SeZjS8zzQ3gd2KmGNCeRH9eMrq8x2jJ1t5xDfZm6W/a62XB9mbtczpm+3JdfnrTlTF+m7pbfDXWzm7xthXkPXNGI+3moPbi3BCc1qnofNkt+IrYx/tfYDz4uqCHZoSHtZwFEpI+qPgJMCMenishB4ZzOcM5M4AYR2aOG+26E66uatNquF5GJIX5FyntvRI70zRGRPUP8ho1STYHXzVRkUX5v13D//ydn+lqh/HKjLdwjL/rmtEDZQc7Kr0AwvT4PM73eS1UX1eteToY0e+TkwUMcgE9jG7MnRHGFzYDbYb7j5wFbRMePx/y0D292/t/N+tpZm+tzfc3O/7tZXztrc33N1YftD6rKmYGH5oamZ8CDhzjQBNeMrs+1uT7X5/ryG9pZm+trfX0eGhvcVMzJFar6OnAy5n3pKyJS+KGpDSLSV1WfAH4CHCIim4lIwTNepT9S2FTaWV87awPX5/ryTTvra2dt4PpaXZ/TWNwdspM7tAGuGZtJO+trZ23g+nB9uaad9bWzNnB9tLg+p3H4wMXJJap6u4gciP369rYi8jNVvTf4Wh8OLAf6AJlslm007ayvnbWB68P15Zp21tfO2sD10eL6nMYgPqB18oyIfBy4ENgBc6cI9gN+e2ttXk5yQTvra2dt4PqalrGMcH2tSztrA9fXtIw5LYEPXJzcIyJbA/tiP0C1DPidqj7d3FxlRzvra2dt4PpaHdfXurSzNnB9jlMKH7g4juM4juM4jpN73KuY4ziO4ziO4zi5xwcujuM4juM4juPkHh+4OI7jOI7jOI6Te3zg4jiO4ziO4zhO7vGBi+M4juM4juM4uccHLo7jOI7jOI7j5B4fuDiO42SEiJwqItpNuL0O9zpARE7OOt16IyLTReQFEekUkVkhbg8ReVhE3hKRzHz0i8g4ETk1w/S2FZHVIjKqimuWishZWeWhFkTkMBF5PHzPi0TkqMTxI0VksYj0alYeHcdxyuEDF8dxnGxZCYxPhMl1uM8BQEsNXERkV+B7wEXAHsCMcOgS4A3gQOz7yopxwHczTO+/gBtU9dkM02wIIrIn8BtgLnAwcCNwlYgcEJ32W0CAYxqfQ8dxnJ7p3ewMOI7jtBkbVPW+ZmeiWkSkv6quq/NtxoS/P1bVVYn4n6rq/9X5/qkRkU2BY4HDmp2XlJwC3KWqU8LnuSKyIzAduBVAVTtFZDY20J7VlFw6juOUwVdcHMdxGoiIdIjINBH5k4isF5GnReTYxDmfFpHbROQVEVklIvfFM+PB/OnfgRGROdqscGyeiFybSG/vcM7Y8Hlk+Hy0iMwWkTeA66PzjxORhSF/y0RkaoXaSl4X8veL8HFluP/ewTSsF3B+rKPSfIjIRBGZKyJvisjKoH8XEZkEXBjOKXxH88LnYSLyq/D9rhORZ0VkRjLtBJ8D1gF3Ju7fX0TOCPlbLyJLROT0Mt/ReBGZIyIvisgaEXlURI5OnLO5iFwqIn8JZl3PicjM6HhV+ReRTYB9gF8lDl0NjBeRzaK43wAfDYMax3GcXOErLo7jOBkjIsm29R1VLezduBCbuT8NeBjYH7hMRP6qqjeEc96PDSTOAjox056bRWSiqt4DXArsAOwLHB6ueTVFVs/CzIM+C7wT8v4t4AfAGcA84GPADBFZq6oXldHc03UzgOWYudW+2CBgEWYadi9wNnBtQUcl+RCRvYHbMPOnY4E1mAnatpgp1NnYAK9gflZY5ZkN9Ae+hpmofYDialAp9gMeUNV3Is0C/G9IfwbwULj3hDLpjADuAS4G3gr5vVxEOlX1qnDOOcAngH8FXgKGAxOjNKrN/yigD/BUIv5JbAJzNDAfQFWfFJEVwCeBhWXSdBzHaTyq6sGDBw8eMgjAqYB2Ez4Zjm+PDUSOTVw3G5hfIs0ObJLp98BlUfxZwNJuzp8HXJuI2zvkY2z4PDJ8vi5x3qbAm8B3E/GnYR3oXiXyWNF1wKRw30GJ8xQ4KUV69wIPAlIiXyfZa26j+DeBz1RZtk8DZybiDgx5P7TMdUuBs0ock1C2lwB3RvFPAJPLpFlV/rHBkQI7J+K3D/EHdFOHflmv58SDBw8e0gY3FXMcx8mWlcBuiXB/OLYfNnC5TkR6FwJwB7BzwZtTMAX6uYi8AGwA3sY244/OOK83Jj6PBwYCv07k705ga2BYiXTSXleKHtMTkYHA7sDPVbVaT2SPAqeLyCQR2a7Ca94HvJaI2xd4XVXnVHpjERkiIheIyDKsXN/GVk7isn0U+JaI/IuIdFfmafJfDa9heh3HcXKFD1wcx3GyZYOqPpgIq8OxLbH9HCspdlrfxjZC9waGikgHMAczFZqO7U3YDbgZ6JdxXl9OfN4y/F2YyN/cED+8RDpprytFJekNwVYsXqwybYCjsJWac4FlYZ/Jfj1c0w9Yn4jbIsX9Z4X7n4kNRncDLqNr2Z4E/A4r/8Ui8oyIfL6G/K8IfzdLxA9JHC+wnuzrmuM4Ts34HhfHcZzG8Tq2grIHtvKS5BXMfGcX4GBVvaVwQET6V3iPt4C+ibgh3Z2ImQkl8wdwCBsPagAWl0gn7XWlqCS9zhCGVpk2qvoCMCkMEsdhJn5zRGQ7Vf1rmTxtnoj7azX3F5F+mKavq+rFUXyXSURVfQOYAkwRkZ2AqcAvRWSBqi5Kkf9nsYHfGCD23DYG+w6fTpy/OcUycBzHyQ0+cHEcx2kcd2IrLpup6m3dnRANUNZHcSOwwc6C6NS/0f2s+PN03cgNNrNfCfdim+a3UdWkGVk9rqspPRG5H/iyiFxUwlzsb+G8fqr6VvKgqnYC94nI94A/YhvnSw1cFmNOE2LuAKaKyCFadKxQjk0wS4e4bAcDh7LxILKQxwXBUcHR2EBjUbX5V9X1IjIXc8JwSXToKOBeVV2ZuGQkcAuO4zg5wwcujuM4DUJVF4vIxcDVInIGZu7TD9gRGK2qx2Gen54HzhaRU4DB2I82vpBI7ilg6+D29wngNVVdClwHfEVEzsX2sOwDHFRh/t4Qc7V8fhgs3UXR69Q+qnp4ltdlkI9pwO2Yx7WfYl7FxgMPhoFEwYvWN0TkTsyr2EuYo4PZ2ErDJpjnsZcwL1uluAcbYMTcFtK6UkQKXuKGAhNV9fhudK0UkfnAdBFZha12TMNMBzctnCcid2Pl+AQ2oPlq0PZAcF2cJv8zgHkich5mhvapELrUjbB3aAz2uy+O4zj5otneATx48OChXQJmsvNaD+cI9ov3C7GZ91cx850vR+fsBjyArTo8g3njmoV1yAvn9AMux8zLFJgVHfs25np4NXAFxRn9pFexQ0rk8UuYa9912P6H+4F/q0B/2euo0KtYNfkA9sIGNmsx18BzCd6zwnd9BvAXbJAwD+voz8RWUNZiG9FvAD7cg7ZdQxrbJeL7Yx7eng/luQT4fnR8KZFXMcwU8A5sIPIcZgbWpd5g+18eD+VX0DQhHEuV/3DtP2KDofXYoO7z3ZxzeLjvwGY/Tx48ePCQDKJarTMWx3Ecx3n3ISKPAVeo6pnNzku9EJGrgDVqq3+O4zi5wgcujuM4jlMBIvJZbDVke1Xd0Oz8ZI2IDMdWcnZS1T81Oz+O4zhJfI+L4ziO41TGtdiv1G8LLGtyXurBMOAEH7Q4jpNXfMXFcRzHcRzHcZzc4z9A6TiO4ziO4zhO7vGBi+M4juM4juM4uccHLo7jOI7jOI7j5B4fuDiO4ziO4ziOk3t84OI4juM4juM4Tu7xgYvjOI7jOI7jOLnn78nNl5ZcMh0pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparisons = [ranked_combined_exact_shap]\n",
    "methods = ['exact_large', 'exact_small']\n",
    "_, fg, df = compare_avg_mag_shap(class_idx,\n",
    "                                 comparisons,\n",
    "                                 ranked_combined_exact_shap_large,\n",
    "                                 methods=methods,\n",
    "                                 bar_width=0.5,\n",
    "                                 legend_fontsize=15,\n",
    "                                 axes_label_fontsize=15,\n",
    "                                 tick_labels_fontsize=15,\n",
    "                                 title=\"Comparison of exact shap values infered from a small (128) and a large (1024) explanation dataset\",\n",
    "                                 title_fontsize=15,\n",
    "                                 xlabel=f'Feature effects (class {class_idx})',\n",
    "                                 ylabel='Features'\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the exact shap values have the same ranking when a larger set is explained, since they are derived from the same model coefficients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparisons = [ranked_avg_shap]\n",
    "methods = ['approx_large', 'approx_small']\n",
    "_, fg, df = compare_avg_mag_shap(class_idx,\n",
    "                                 comparisons,\n",
    "                                 ranked_avg_shap_l,\n",
    "                                 methods=methods,\n",
    "                                 bar_width=0.5,\n",
    "                                 legend_fontsize=15,\n",
    "                                 axes_label_fontsize=15,\n",
    "                                 tick_labels_fontsize=15,\n",
    "                                 title=\"Comparison of approximate shap values infered from a small (128) and a large (1024) explanation dataset\",\n",
    "                                 title_fontsize=15,\n",
    "                                 xlabel=f'Feature effects (class {class_idx})',\n",
    "                                 ylabel='Features'\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ranking of the features also remains unchanged for the approximate method even when significantly more instances are explained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('large_explain_set.pkl', 'wb') as f:\n",
    "    pickle.dump(\n",
    "        {'data': data,\n",
    "         'explainer': explainer,\n",
    "         'raw_shap': explanation_large_dataset,\n",
    "         'ranked_shap_vals': ranked_avg_shap_l\n",
    "        },\n",
    "        f\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Footnotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Footnotes'></a>\n",
    "\n",
    "[(1)](#f_1): As detailed in *Theorem 1* in [[3]](#References), the estimation process for a shap value of feature $i$ from instance $x$ involves taking a weighted average of the contribution of feature $i$ to the model output, where the weighting takes into account all the possible orderings in which the previous and successor features can be added to the set. This computation is thus performed by choosing subsets of features from the full feature set and setting the values of these features to a *background value*; the prediction on these perturbed samples is used in  a least squares objective (*Theorem 2*), weighted by the Shapley kernel. Note that the optimisation objective involves a summation over all possible subsets. Enumerating all the feature subsets has exponential computational cost, so the smaller the feature set, the more samples can be drawn and more accurate shap values can be estimated. Thus, grouping the features can serve to reduce the variance of the shap values estimation by providing a smaller set of features to choose from.\n",
    "\n",
    "[(2)](#f_2): This is a kwarg to `shap_values` method.\n",
    "\n",
    "[(3)](#f_3): Note the progress bars below show, however, different runtimes between the two methods. No accurate timing analysis was carried out to study this aspect.\n",
    "\n",
    "[(4)](#f_4): Note that the `shap` library currently does not support grouping when the data is represented as a sparse matrix, so it should be converted to a `numpy.ndarray` object, both during explainer initialisation and when calling the `shap_values` method. \n",
    "\n",
    "[(5)](#f_5): When `link='logit'` is passed to the plotting function, the model outputs are scaled to the probability space, so the _inverse logit transformation_ is applied to the data and axis ticks. This is in contrast to passing `link='logit'` to the KernelExplainer, which maps the model output through the *forward logit transformation*,\n",
    "$\\log \\left( \\frac{p}{1-p} \\right)$.\n",
    "\n",
    "[(6)](#f_6): We could alter the base value by specifying the `new_base_value` argument to  `shap.decision_plot`. Note that this argument has to be specified in the *same* units as the explanation - if we explained the instances in margin space then to switch the base value of the plot to, say, `p=0.4` then we would pass `new_base_value = log(0.4/(1 - 0.4))` to the plotting function.\n",
    "\n",
    "[(7)](#f_7): In this context, bias refers to the bias-variance tradeoff; a simpler model will likely incur a larger error during training but will have a smaller generalisation gap compared to a more complex model which will have smaller training error but will generalise poorly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#src_1) *Mahto, K.K., 2019. \"One-Hot-Encoding, Multicollinearity and the Dummy Variable Trap\". Retrieved 02 Feb 2020* [(link)](https://towardsdatascience.com/one-hot-encoding-multicollinearity-and-the-dummy-variable-trap-b5840be3c41a)\n",
    "\n",
    "[[2]](#src_2) *Mood, C., 2017. \"Logistic regression: Uncovering unobserved heterogeneity.\"*\n",
    "\n",
    "[[3]](#src_3) *Lundberg, S.M. and Lee, S.I., 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).*\n",
    "\n",
    "[[4]](#src_4) *Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. and Lee, S.I., 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), pp.56-67.*\n",
    "\n",
    "[[5]](#https://www.nature.com/articles/s41551-018-0304-0) *Lundberg, S.M., Nair, B., Vavilala, M.S., Horibe, M., Eisses, M.J., Adams, T., Liston, D.E., Low, D.K.W., Newman, S.F., Kim, J. and Lee, S.I., 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering, 2(10), pp.749-760.*"
   ]
  }
 ],
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